CINXE.COM

<!DOCTYPE html> <html lang="en" dir="ltr"> <head>  <script async src="https://www.googletagmanager.com/gtag/js?id=G-P63WKM1TM1"></script> <script> window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'G-P63WKM1TM1'); </script>  <script type="text/javascript" > (function(m,e,t,r,i,k,a){m[i]=m[i]||function(){(m[i].a=m[i].a||[]).push(arguments)}; m[i].l=1*new Date(); for (var j = 0; j < document.scripts.length; j++) {if (document.scripts[j].src === r) { return; }} k=e.createElement(t),a=e.getElementsByTagName(t)[0],k.async=1,k.src=r,a.parentNode.insertBefore(k,a)}) (window, document, "script", "https://mc.yandex.ru/metrika/tag.js", "ym"); ym(55165297, "init", { clickmap:false, trackLinks:true, accurateTrackBounce:true, webvisor:false }); </script> <noscript><div><img src="https://mc.yandex.ru/watch/55165297" style="position:absolute; left:-9999px;" alt="" /></div></noscript>    <title>Search results for: saddlepoint approximation</title> <meta name="description" content="Search results for: saddlepoint approximation"> <meta name="keywords" content="saddlepoint approximation"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler ml-auto" type="button" data-toggle="collapse" data-target="#navbarMenu" aria-controls="navbarMenu" aria-expanded="false" aria-label="Toggle navigation"> <span class="navbar-toggler-icon"></span> </button> <div class="w-100"> <div class="d-none d-lg-flex flex-row-reverse"> <form method="get" action="https://waset.org/search" class="form-inline my-2 my-lg-0"> <input class="form-control mr-sm-2" type="search" placeholder="Search Conferences" value="saddlepoint approximation" name="q" aria-label="Search"> <button class="btn btn-light my-2 my-sm-0" type="submit"><i class="fas fa-search"></i></button> </form> </div> <div class="collapse navbar-collapse mt-1" id="navbarMenu"> <ul class="navbar-nav ml-auto align-items-center" id="mainNavMenu"> <li class="nav-item"> <a class="nav-link" href="https://waset.org/conferences" title="Conferences in 2024/2025/2026">Conferences</a> </li> <li class="nav-item"> <a class="nav-link" href="https://waset.org/disciplines" title="Disciplines">Disciplines</a> </li> <li class="nav-item"> <a class="nav-link" href="https://waset.org/committees" rel="nofollow">Committees</a> </li> <li class="nav-item dropdown"> <a class="nav-link dropdown-toggle" href="#" id="navbarDropdownPublications" role="button" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false"> Publications </a> <div class="dropdown-menu" aria-labelledby="navbarDropdownPublications"> <a class="dropdown-item" href="https://publications.waset.org/abstracts">Abstracts</a> <a class="dropdown-item" href="https://publications.waset.org">Periodicals</a> <a class="dropdown-item" href="https://publications.waset.org/archive">Archive</a> </div> </li> <li class="nav-item"> <a class="nav-link" href="https://waset.org/page/support" title="Support">Support</a> </li> </ul> </div> </div> </nav> </div> </header> <main> <div class="container mt-4"> <div class="row"> <div class="col-md-9 mx-auto"> <form method="get" action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="saddlepoint approximation"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 517</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: saddlepoint approximation</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">517</span> Polynomially Adjusted Bivariate Density Estimates Based on the Saddlepoint Approximation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20B.%20Provost">S. B. Provost</a>, <a href="https://publications.waset.org/abstracts/search?q=Susan%20Sheng"> Susan Sheng</a> </p> <p class="card-text"><strong>Abstract:</strong></p> An alternative bivariate density estimation methodology is introduced in this presentation. The proposed approach involves estimating the density function associated with the marginal distribution of each of the two variables by means of the saddlepoint approximation technique and applying a bivariate polynomial adjustment to the product of these density estimates. Since the saddlepoint approximation is utilized in the context of density estimation, such estimates are determined from empirical cumulant-generating functions. In the univariate case, the saddlepoint density estimate is itself adjusted by a polynomial. Given a set of observations, the coefficients of the polynomial adjustments are obtained from the sample moments. Several illustrative applications of the proposed methodology shall be presented. Since this approach relies essentially on a determinate number of sample moments, it is particularly well suited for modeling massive data sets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=density%20estimation" title="density estimation">density estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=empirical%20cumulant-generating%20function" title=" empirical cumulant-generating function"> empirical cumulant-generating function</a>, <a href="https://publications.waset.org/abstracts/search?q=moments" title=" moments"> moments</a>, <a href="https://publications.waset.org/abstracts/search?q=saddlepoint%20approximation" title=" saddlepoint approximation"> saddlepoint approximation</a> </p> <a href="https://publications.waset.org/abstracts/72664/polynomially-adjusted-bivariate-density-estimates-based-on-the-saddlepoint-approximation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72664.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">280</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">516</span> On Modeling Data Sets by Means of a Modified Saddlepoint Approximation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Serge%20B.%20Provost">Serge B. Provost</a>, <a href="https://publications.waset.org/abstracts/search?q=Yishan%20Zhang"> Yishan Zhang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A moment-based adjustment to the saddlepoint approximation is introduced in the context of density estimation. First applied to univariate distributions, this methodology is extended to the bivariate case. It then entails estimating the density function associated with each marginal distribution by means of the saddlepoint approximation and applying a bivariate adjustment to the product of the resulting density estimates. The connection to the distribution of empirical copulas will be pointed out. As well, a novel approach is proposed for estimating the support of distribution. As these results solely rely on sample moments and empirical cumulant-generating functions, they are particularly well suited for modeling massive data sets. Several illustrative applications will be presented. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=empirical%20cumulant-generating%20function" title="empirical cumulant-generating function">empirical cumulant-generating function</a>, <a href="https://publications.waset.org/abstracts/search?q=endpoints%20identification" title=" endpoints identification"> endpoints identification</a>, <a href="https://publications.waset.org/abstracts/search?q=saddlepoint%20approximation" title=" saddlepoint approximation"> saddlepoint approximation</a>, <a href="https://publications.waset.org/abstracts/search?q=sample%20moments" title=" sample moments"> sample moments</a>, <a href="https://publications.waset.org/abstracts/search?q=density%20estimation" title=" density estimation"> density estimation</a> </p> <a href="https://publications.waset.org/abstracts/144553/on-modeling-data-sets-by-means-of-a-modified-saddlepoint-approximation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/144553.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">162</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">515</span> Finite Sample Inferences for Weak Instrument Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gubhinder%20Kundhi">Gubhinder Kundhi</a>, <a href="https://publications.waset.org/abstracts/search?q=Paul%20Rilstone"> Paul Rilstone</a> </p> <p class="card-text"><strong>Abstract:</strong></p> It is well established that Instrumental Variable (IV) estimators in the presence of weak instruments can be poorly behaved, in particular, be quite biased in finite samples. Finite sample approximations to the distributions of these estimators are obtained using Edgeworth and Saddlepoint expansions. Departures from normality of the distributions of these estimators are analyzed using higher order analytical corrections in these expansions. In a Monte-Carlo experiment, the performance of these expansions is compared to the first order approximation and other methods commonly used in finite samples such as the bootstrap. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bootstrap" title="bootstrap">bootstrap</a>, <a href="https://publications.waset.org/abstracts/search?q=Instrumental%20Variable" title=" Instrumental Variable"> Instrumental Variable</a>, <a href="https://publications.waset.org/abstracts/search?q=Edgeworth%20expansions" title=" Edgeworth expansions"> Edgeworth expansions</a>, <a href="https://publications.waset.org/abstracts/search?q=Saddlepoint%20expansions" title=" Saddlepoint expansions"> Saddlepoint expansions</a> </p> <a href="https://publications.waset.org/abstracts/46824/finite-sample-inferences-for-weak-instrument-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/46824.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">310</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">514</span> Refined Procedures for Second Order Asymptotic Theory</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gubhinder%20Kundhi">Gubhinder Kundhi</a>, <a href="https://publications.waset.org/abstracts/search?q=Paul%20Rilstone"> Paul Rilstone</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Refined procedures for higher-order asymptotic theory for non-linear models are developed. These include a new method for deriving stochastic expansions of arbitrary order, new methods for evaluating the moments of polynomials of sample averages, a new method for deriving the approximate moments of the stochastic expansions; an application of these techniques to gather improved inferences with the weak instruments problem is considered. It is well established that Instrumental Variable (IV) estimators in the presence of weak instruments can be poorly behaved, in particular, be quite biased in finite samples. In our application, finite sample approximations to the distributions of these estimators are obtained using Edgeworth and Saddlepoint expansions. Departures from normality of the distributions of these estimators are analyzed using higher order analytical corrections in these expansions. In a Monte-Carlo experiment, the performance of these expansions is compared to the first order approximation and other methods commonly used in finite samples such as the bootstrap. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=edgeworth%20expansions" title="edgeworth expansions">edgeworth expansions</a>, <a href="https://publications.waset.org/abstracts/search?q=higher%20order%20asymptotics" title=" higher order asymptotics"> higher order asymptotics</a>, <a href="https://publications.waset.org/abstracts/search?q=saddlepoint%20expansions" title=" saddlepoint expansions"> saddlepoint expansions</a>, <a href="https://publications.waset.org/abstracts/search?q=weak%20instruments" title=" weak instruments"> weak instruments</a> </p> <a href="https://publications.waset.org/abstracts/68155/refined-procedures-for-second-order-asymptotic-theory" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/68155.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">277</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">513</span> Approximation Property Pass to Free Product</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kankeyanathan%20Kannan">Kankeyanathan Kannan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> On approximation properties of group C* algebras is everywhere; it is powerful, important, backbone of countless breakthroughs. For a discrete group G, let A(G) denote its Fourier algebra, and let M₀A(G) denote the space of completely bounded Fourier multipliers on G. An approximate identity on G is a sequence (Φn) of finitely supported functions such that (Φn) uniformly converge to constant function 1 In this paper we prove that approximation property pass to free product. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=approximation%20property" title="approximation property">approximation property</a>, <a href="https://publications.waset.org/abstracts/search?q=weakly%20amenable" title=" weakly amenable"> weakly amenable</a>, <a href="https://publications.waset.org/abstracts/search?q=strong%20invariant%20approximation%20property" title=" strong invariant approximation property"> strong invariant approximation property</a>, <a href="https://publications.waset.org/abstracts/search?q=invariant%20approximation%20property" title=" invariant approximation property"> invariant approximation property</a> </p> <a href="https://publications.waset.org/abstracts/44414/approximation-property-pass-to-free-product" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/44414.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">675</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">512</span> Modified Approximation Methods for Finding an Optimal Solution for the Transportation Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=N.%20Guruprasad">N. Guruprasad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a modification of approximation method for transportation problems. The initial basic feasible solution can be computed using either Russel's or Vogel's approximation methods. Russell’s approximation method provides another excellent criterion that is still quick to implement on a computer (not manually) In most cases Russel's method yields a better initial solution, though it takes longer than Vogel's method (finding the next entering variable in Russel's method is in O(n1*n2), and in O(n1+n2) for Vogel's method). However, Russel's method normally has a lesser total running time because less pivots are required to reach the optimum for all but small problem sizes (n1+n2=~20). With this motivation behind we have incorporated a variation of the same – what we have proposed it has TMC (Total Modified Cost) to obtain fast and efficient solutions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=computation" title="computation">computation</a>, <a href="https://publications.waset.org/abstracts/search?q=efficiency" title=" efficiency"> efficiency</a>, <a href="https://publications.waset.org/abstracts/search?q=modified%20cost" title=" modified cost"> modified cost</a>, <a href="https://publications.waset.org/abstracts/search?q=Russell%E2%80%99s%20approximation%20method" title=" Russell’s approximation method"> Russell’s approximation method</a>, <a href="https://publications.waset.org/abstracts/search?q=transportation" title=" transportation"> transportation</a>, <a href="https://publications.waset.org/abstracts/search?q=Vogel%E2%80%99s%20approximation%20method" title=" Vogel’s approximation method"> Vogel’s approximation method</a> </p> <a href="https://publications.waset.org/abstracts/19162/modified-approximation-methods-for-finding-an-optimal-solution-for-the-transportation-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19162.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">547</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">511</span> Constant Factor Approximation Algorithm for p-Median Network Design Problem with Multiple Cable Types</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chaghoub%20Soraya">Chaghoub Soraya</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhang%20Xiaoyan"> Zhang Xiaoyan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This research presents the first constant approximation algorithm to the p-median network design problem with multiple cable types. This problem was addressed with a single cable type and there is a bifactor approximation algorithm for the problem. To the best of our knowledge, the algorithm proposed in this paper is the first constant approximation algorithm for the p-median network design with multiple cable types. The addressed problem is a combination of two well studied problems which are p-median problem and network design problem. The introduced algorithm is a random sampling approximation algorithm of constant factor which is conceived by using some random sampling techniques form the literature. It is based on a redistribution Lemma from the literature and a steiner tree problem as a subproblem. This algorithm is simple, and it relies on the notions of random sampling and probability. The proposed approach gives an approximation solution with one constant ratio without violating any of the constraints, in contrast to the one proposed in the literature. This paper provides a (21 + 2)-approximation algorithm for the p-median network design problem with multiple cable types using random sampling techniques. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=approximation%20algorithms" title="approximation algorithms">approximation algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=buy-at-bulk" title=" buy-at-bulk"> buy-at-bulk</a>, <a href="https://publications.waset.org/abstracts/search?q=combinatorial%20optimization" title=" combinatorial optimization"> combinatorial optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=network%20design" title=" network design"> network design</a>, <a href="https://publications.waset.org/abstracts/search?q=p-median" title=" p-median"> p-median</a> </p> <a href="https://publications.waset.org/abstracts/127337/constant-factor-approximation-algorithm-for-p-median-network-design-problem-with-multiple-cable-types" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/127337.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">203</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">510</span> Approximation of the Time Series by Fractal Brownian Motion</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Valeria%20Bondarenko">Valeria Bondarenko</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we propose two problems related to fractal Brownian motion. First problem is simultaneous estimation of two parameters, Hurst exponent and the volatility, that describe this random process. Numerical tests for the simulated fBm provided an efficient method. Second problem is approximation of the increments of the observed time series by a power function by increments from the fractional Brownian motion. Approximation and estimation are shown on the example of real data, daily deposit interest rates. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fractional%20Brownian%20motion" title="fractional Brownian motion">fractional Brownian motion</a>, <a href="https://publications.waset.org/abstracts/search?q=Gausssian%20processes" title=" Gausssian processes"> Gausssian processes</a>, <a href="https://publications.waset.org/abstracts/search?q=approximation" title=" approximation"> approximation</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20series" title=" time series"> time series</a>, <a href="https://publications.waset.org/abstracts/search?q=estimation%20of%20properties%20of%20the%20model" title=" estimation of properties of the model"> estimation of properties of the model</a> </p> <a href="https://publications.waset.org/abstracts/4285/approximation-of-the-time-series-by-fractal-brownian-motion" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/4285.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">376</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">509</span> Approximation of Periodic Functions Belonging to Lipschitz Classes by Product Matrix Means of Fourier Series</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Smita%20Sonker">Smita Sonker</a>, <a href="https://publications.waset.org/abstracts/search?q=Uaday%20Singh"> Uaday Singh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Various investigators have determined the degree of approximation of functions belonging to the classes W(L r , ξ(t)), Lip(ξ(t), r), Lip(α, r), and Lipα using different summability methods with monotonocity conditions. Recently, Lal has determined the degree of approximation of the functions belonging to Lipα and W(L r , ξ(t)) classes by using Ces`aro-N¨orlund (C 1 .Np)- summability with non-increasing weights {pn}. In this paper, we shall determine the degree of approximation of 2π - periodic functions f belonging to the function classes Lipα and W(L r , ξ(t)) by C 1 .T - means of Fourier series of f. Our theorems generalize the results of Lal and we also improve these results in the light off. From our results, we also derive some corollaries. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lipschitz%20classes" title="Lipschitz classes">Lipschitz classes</a>, <a href="https://publications.waset.org/abstracts/search?q=product%20matrix%20operator" title=" product matrix operator"> product matrix operator</a>, <a href="https://publications.waset.org/abstracts/search?q=signals" title=" signals"> signals</a>, <a href="https://publications.waset.org/abstracts/search?q=trigonometric%20Fourier%20approximation" title=" trigonometric Fourier approximation"> trigonometric Fourier approximation</a> </p> <a href="https://publications.waset.org/abstracts/4757/approximation-of-periodic-functions-belonging-to-lipschitz-classes-by-product-matrix-means-of-fourier-series" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/4757.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">477</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">508</span> High-Pressure Calculations of the Elastic Properties of ZnSx Se 1−x Alloy in the Virtual-Crystal Approximation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=N.%20Lebga">N. Lebga</a>, <a href="https://publications.waset.org/abstracts/search?q=Kh.%20Bouamama"> Kh. Bouamama</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Kassali"> K. Kassali</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We report first-principles calculation results on the structural and elastic properties of ZnS x Se1−x alloy for which we employed the virtual crystal approximation provided with the ABINIT program. The calculations done using density functional theory within the local density approximation and employing the virtual-crystal approximation, we made a comparative study between the numerical results obtained from ab-initio calculation using ABINIT or Wien2k within the Density Functional Theory framework with either Local Density Approximation or Generalized Gradient approximation and the pseudo-potential plane-wave method with the Hartwigzen Goedecker Hutter scheme potentials. It is found that the lattice parameter, the phase transition pressure, and the elastic constants (and their derivative with respect to the pressure) follow a quadratic law in x. The variation of the elastic constants is also numerically studied and the phase transformations are discussed in relation to the mechanical stability criteria. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=density%20functional%20theory" title="density functional theory">density functional theory</a>, <a href="https://publications.waset.org/abstracts/search?q=elastic%20properties" title=" elastic properties"> elastic properties</a>, <a href="https://publications.waset.org/abstracts/search?q=ZnS" title=" ZnS"> ZnS</a>, <a href="https://publications.waset.org/abstracts/search?q=ZnSe" title=" ZnSe"> ZnSe</a>, <a href="https://publications.waset.org/abstracts/search?q=" title=" "> </a> </p> <a href="https://publications.waset.org/abstracts/33371/high-pressure-calculations-of-the-elastic-properties-of-znsx-se-1x-alloy-in-the-virtual-crystal-approximation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33371.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">574</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">507</span> Approximation of Convex Set by Compactly Semidefinite Representable Set</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Anusuya%20Ghosh">Anusuya Ghosh</a>, <a href="https://publications.waset.org/abstracts/search?q=Vishnu%20Narayanan"> Vishnu Narayanan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The approximation of convex set by semidefinite representable set plays an important role in semidefinite programming, especially in modern convex optimization. To optimize a linear function over a convex set is a hard problem. But optimizing the linear function over the semidefinite representable set which approximates the convex set is easy to solve as there exists numerous efficient algorithms to solve semidefinite programming problems. So, our approximation technique is significant in optimization. We develop a technique to approximate any closed convex set, say K by compactly semidefinite representable set. Further we prove that there exists a sequence of compactly semidefinite representable sets which give tighter approximation of the closed convex set, K gradually. We discuss about the convergence of the sequence of compactly semidefinite representable sets to closed convex set K. The recession cone of K and the recession cone of the compactly semidefinite representable set are equal. So, we say that the sequence of compactly semidefinite representable sets converge strongly to the closed convex set. Thus, this approximation technique is very useful development in semidefinite programming. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=semidefinite%20programming" title="semidefinite programming">semidefinite programming</a>, <a href="https://publications.waset.org/abstracts/search?q=semidefinite%20representable%20set" title=" semidefinite representable set"> semidefinite representable set</a>, <a href="https://publications.waset.org/abstracts/search?q=compactly%20semidefinite%20representable%20set" title=" compactly semidefinite representable set"> compactly semidefinite representable set</a>, <a href="https://publications.waset.org/abstracts/search?q=approximation" title=" approximation"> approximation</a> </p> <a href="https://publications.waset.org/abstracts/36914/approximation-of-convex-set-by-compactly-semidefinite-representable-set" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36914.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">386</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">506</span> Approximation of Analytic Functions of Several Variables by Linear K-Positive Operators in the Closed Domain</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tulin%20Coskun">Tulin Coskun</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We investigate the approximation of analytic functions of several variables in polydisc by the sequences of linear k-positive operators in Gadjiev sence. The approximation of analytic functions of complex variable by linear k-positive operators was tackled, and k-positive operators and formulated theorems of Korovkin's type for these operators in the space of analytic functions on the unit disc were introduced in the past. Recently, very general results on convergence of the sequences of linear k-positive operators on a simply connected bounded domain within the space of analytic functions were proved. In this presentation, we extend some of these results to the approximation of analytic functions of several complex variables by sequences of linear k-positive operators. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=analytic%20functions" title="analytic functions">analytic functions</a>, <a href="https://publications.waset.org/abstracts/search?q=approximation%20of%20analytic%20functions" title=" approximation of analytic functions"> approximation of analytic functions</a>, <a href="https://publications.waset.org/abstracts/search?q=Linear%20k-positive%20operators" title=" Linear k-positive operators"> Linear k-positive operators</a>, <a href="https://publications.waset.org/abstracts/search?q=Korovkin%20type%20theorems" title=" Korovkin type theorems"> Korovkin type theorems</a> </p> <a href="https://publications.waset.org/abstracts/53219/approximation-of-analytic-functions-of-several-variables-by-linear-k-positive-operators-in-the-closed-domain" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/53219.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">338</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">505</span> Degree of Approximation of Functions by Product Means</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hare%20Krishna%20Nigam">Hare Krishna Nigam</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, for the first time, (E,q)(C,2) product summability method is introduced and two quite new results on degree of approximation of the function f belonging to Lip (alpha,r)class and W(L(r), xi(t)) class by (E,q)(C,2) product means of Fourier series, has been obtained. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Degree%20of%20approximation" title="Degree of approximation">Degree of approximation</a>, <a href="https://publications.waset.org/abstracts/search?q=%28E" title=" (E"> (E</a>, <a href="https://publications.waset.org/abstracts/search?q=q%29%28C" title="q)(C">q)(C</a>, <a href="https://publications.waset.org/abstracts/search?q=2%29%20means" title="2) means">2) means</a>, <a href="https://publications.waset.org/abstracts/search?q=Fourier%20series" title=" Fourier series"> Fourier series</a>, <a href="https://publications.waset.org/abstracts/search?q=Lebesgue%20integral" title=" Lebesgue integral"> Lebesgue integral</a>, <a href="https://publications.waset.org/abstracts/search?q=Lip%20%28alpha" title=" Lip (alpha"> Lip (alpha</a>, <a href="https://publications.waset.org/abstracts/search?q=r%29class" title="r)class">r)class</a>, <a href="https://publications.waset.org/abstracts/search?q=W%28L%28r%29" title=" W(L(r)"> W(L(r)</a>, <a href="https://publications.waset.org/abstracts/search?q=xi%28t%29%29class%20of%20%20functions" title="xi(t))class of functions">xi(t))class of functions</a> </p> <a href="https://publications.waset.org/abstracts/32235/degree-of-approximation-of-functions-by-product-means" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/32235.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">517</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">504</span> Approximation to the Hardy Operator on Topological Measure Spaces</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kairat%20T.%20Mynbaev">Kairat T. Mynbaev</a>, <a href="https://publications.waset.org/abstracts/search?q=Elena%20N.%20Lomakina"> Elena N. Lomakina</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We consider a Hardy-type operator generated by a family of open subsets of a Hausdorff topological space. The family is indexed with non-negative real numbers and is totally ordered. For this operator, we obtain two-sided bounds of its norm, a compactness criterion, and bounds for its approximation numbers. Previously, bounds for its approximation numbers have been established only in the one-dimensional case, while we do not impose any restrictions on the dimension of the Hausdorff space. The bounds for the norm and conditions for compactness earlier have been found using different methods by G. Sinnamon and K. Mynbaev. Our approach is different in that we use domain partitions for all problems under consideration. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=approximation%20numbers" title="approximation numbers">approximation numbers</a>, <a href="https://publications.waset.org/abstracts/search?q=boundedness%20and%20compactness" title=" boundedness and compactness"> boundedness and compactness</a>, <a href="https://publications.waset.org/abstracts/search?q=multidimensional%20Hardy%20operator" title=" multidimensional Hardy operator"> multidimensional Hardy operator</a>, <a href="https://publications.waset.org/abstracts/search?q=Hausdorff%20topological%20space" title=" Hausdorff topological space"> Hausdorff topological space</a> </p> <a href="https://publications.waset.org/abstracts/170957/approximation-to-the-hardy-operator-on-topological-measure-spaces" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/170957.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">104</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">503</span> Degree of Approximation by the (T.E^1) Means of Conjugate Fourier Series in the Hölder Metric</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kejal%20Khatri">Kejal Khatri</a>, <a href="https://publications.waset.org/abstracts/search?q=Vishnu%20Narayan%20Mishra"> Vishnu Narayan Mishra</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We compute the degree of approximation of functions\tilde{f}\in H_w, a new Banach space using (T.E^1) summability means of conjugate Fourier series. In this paper, we extend the results of Singh and Mahajan which in turn generalizes the result of Lal and Yadav. Some corollaries have also been deduced from our main theorem and particular cases. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=conjugate%20Fourier%20series" title="conjugate Fourier series">conjugate Fourier series</a>, <a href="https://publications.waset.org/abstracts/search?q=degree%20of%20approximation" title=" degree of approximation"> degree of approximation</a>, <a href="https://publications.waset.org/abstracts/search?q=H%C3%B6lder%20metric" title=" Hölder metric"> Hölder metric</a>, <a href="https://publications.waset.org/abstracts/search?q=matrix%20summability" title=" matrix summability"> matrix summability</a>, <a href="https://publications.waset.org/abstracts/search?q=product%20summability" title=" product summability"> product summability</a> </p> <a href="https://publications.waset.org/abstracts/2123/degree-of-approximation-by-the-te1-means-of-conjugate-fourier-series-in-the-holder-metric" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2123.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">420</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">502</span> Degree of Approximation of Functions Conjugate to Periodic Functions Belonging to Lipschitz Classes by Product Matrix Means</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Smita%20Sonker">Smita Sonker</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Various investigators have determined the degree of approximation of conjugate signals (functions) of functions belonging to different classes Lipα, Lip(α,p), Lip(ξ(t),p), W(Lr,ξ(t), (β ≥ 0)) by matrix summability means, lower triangular matrix operator, product means (i.e. (C,1)(E,1), (C,1)(E,q), (E,q)(C,1) (N,p,q)(E,1), and (E,q)(N,pn) of their conjugate trigonometric Fourier series. In this paper, we shall determine the degree of approximation of 2π-periodic function conjugate functions of f belonging to the function classes Lipα and W(Lr; ξ(t); (β ≥ 0)) by (C1.T) -means of their conjugate trigonometric Fourier series. On the other hand, we shall review above-mentioned work in the light of Lenski. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=signals" title="signals">signals</a>, <a href="https://publications.waset.org/abstracts/search?q=trigonometric%20fourier%20approximation" title=" trigonometric fourier approximation"> trigonometric fourier approximation</a>, <a href="https://publications.waset.org/abstracts/search?q=class%20W%28L%5Er" title=" class W(L^r"> class W(L^r</a>, <a href="https://publications.waset.org/abstracts/search?q=%5Cxi%28t%29" title="\xi(t)">\xi(t)</a>, <a href="https://publications.waset.org/abstracts/search?q=conjugate%20fourier%20series" title=" conjugate fourier series"> conjugate fourier series</a> </p> <a href="https://publications.waset.org/abstracts/20996/degree-of-approximation-of-functions-conjugate-to-periodic-functions-belonging-to-lipschitz-classes-by-product-matrix-means" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/20996.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">397</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">501</span> Orthogonal Basis Extreme Learning Algorithm and Function Approximation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ying%20Li">Ying Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Yan%20Li"> Yan Li</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A new algorithm for single hidden layer feedforward neural networks (SLFN), Orthogonal Basis Extreme Learning (OBEL) algorithm, is proposed and the algorithm derivation is given in the paper. The algorithm can decide both the NNs parameters and the neuron number of hidden layer(s) during training while providing extreme fast learning speed. It will provide a practical way to develop NNs. The simulation results of function approximation showed that the algorithm is effective and feasible with good accuracy and adaptability. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title="neural network">neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=orthogonal%20basis%20extreme%20learning" title=" orthogonal basis extreme learning"> orthogonal basis extreme learning</a>, <a href="https://publications.waset.org/abstracts/search?q=function%20approximation" title=" function approximation"> function approximation</a> </p> <a href="https://publications.waset.org/abstracts/15129/orthogonal-basis-extreme-learning-algorithm-and-function-approximation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15129.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">534</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">500</span> Structural and Electronic Properties of the Rock-salt BaxSr1−xS Alloys</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=B.%20Bahloul">B. Bahloul</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Babesse"> K. Babesse</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Dkhira"> A. Dkhira</a>, <a href="https://publications.waset.org/abstracts/search?q=Y.%20Bahloul"> Y. Bahloul</a>, <a href="https://publications.waset.org/abstracts/search?q=L.%20Amirouche"> L. Amirouche </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Structural and electronic properties of the rock-salt BaxSr1−xS are calculated using the first-principles calculations based on the density functional theory (DFT) within the generalized gradient approximation (GGA), the local density approximation (LDA) and the virtual-crystal approximation (VCA). The calculated lattice parameters at equilibrium volume for x=0 and x=1 are in good agreement with the literature data. The BaxSr1−xS alloys are found to be an indirect band gap semiconductor. Moreoever, for the composition (x) ranging between [0-1], we think that our results are well discussed and well predicted. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=semiconductor" title="semiconductor">semiconductor</a>, <a href="https://publications.waset.org/abstracts/search?q=Ab%20initio%20calculations" title=" Ab initio calculations"> Ab initio calculations</a>, <a href="https://publications.waset.org/abstracts/search?q=rocksalt" title=" rocksalt"> rocksalt</a>, <a href="https://publications.waset.org/abstracts/search?q=band%20structure" title=" band structure"> band structure</a>, <a href="https://publications.waset.org/abstracts/search?q=BaxSr1%E2%88%92xS" title=" BaxSr1−xS"> BaxSr1−xS</a> </p> <a href="https://publications.waset.org/abstracts/13545/structural-and-electronic-properties-of-the-rock-salt-baxsr1xs-alloys" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13545.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">395</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">499</span> An Optimized RDP Algorithm for Curve Approximation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jean-Pierre%20Lomaliza">Jean-Pierre Lomaliza</a>, <a href="https://publications.waset.org/abstracts/search?q=Kwang-Seok%20Moon"> Kwang-Seok Moon</a>, <a href="https://publications.waset.org/abstracts/search?q=Hanhoon%20Park"> Hanhoon Park</a> </p> <p class="card-text"><strong>Abstract:</strong></p> It is well-known that Ramer Douglas Peucker (RDP) algorithm greatly depends on the method of choosing starting points. Therefore, this paper focuses on finding such starting points that will optimize the results of RDP algorithm. Specifically, this paper proposes a curve approximation algorithm that finds flat points, called essential points, of an input curve, divides the curve into corner-like sub-curves using the essential points, and applies the RDP algorithm to the sub-curves. The number of essential points play a role on optimizing the approximation results by balancing the degree of shape information loss and the amount of data reduction. Through experiments with curves of various types and complexities of shape, we compared the performance of the proposed algorithm with three other methods, i.e., the RDP algorithm itself and its variants. As a result, the proposed algorithm outperformed the others in term of maintaining the original shapes of the input curve, which is important in various applications like pattern recognition. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=curve%20approximation" title="curve approximation">curve approximation</a>, <a href="https://publications.waset.org/abstracts/search?q=essential%20point" title=" essential point"> essential point</a>, <a href="https://publications.waset.org/abstracts/search?q=RDP%20algorithm" title=" RDP algorithm"> RDP algorithm</a> </p> <a href="https://publications.waset.org/abstracts/29359/an-optimized-rdp-algorithm-for-curve-approximation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29359.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">535</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">498</span> The Profit Trend of Cosmetics Products Using Bootstrap Edgeworth Approximation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Edlira%20Donefski">Edlira Donefski</a>, <a href="https://publications.waset.org/abstracts/search?q=Lorenc%20Ekonomi"> Lorenc Ekonomi</a>, <a href="https://publications.waset.org/abstracts/search?q=Tina%20Donefski"> Tina Donefski</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Edgeworth approximation is one of the most important statistical methods that has a considered contribution in the reduction of the sum of standard deviation of the independent variables’ coefficients in a Quantile Regression Model. This model estimates the conditional median or other quantiles. In this paper, we have applied approximating statistical methods in an economical problem. We have created and generated a quantile regression model to see how the profit gained is connected with the realized sales of the cosmetic products in a real data, taken from a local business. The Linear Regression of the generated profit and the realized sales was not free of autocorrelation and heteroscedasticity, so this is the reason that we have used this model instead of Linear Regression. Our aim is to analyze in more details the relation between the variables taken into study: the profit and the finalized sales and how to minimize the standard errors of the independent variable involved in this study, the level of realized sales. The statistical methods that we have applied in our work are Edgeworth Approximation for Independent and Identical distributed (IID) cases, Bootstrap version of the Model and the Edgeworth approximation for Bootstrap Quantile Regression Model. The graphics and the results that we have presented here identify the best approximating model of our study. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bootstrap" title="bootstrap">bootstrap</a>, <a href="https://publications.waset.org/abstracts/search?q=edgeworth%20approximation" title=" edgeworth approximation"> edgeworth approximation</a>, <a href="https://publications.waset.org/abstracts/search?q=IID" title=" IID"> IID</a>, <a href="https://publications.waset.org/abstracts/search?q=quantile" title=" quantile"> quantile</a> </p> <a href="https://publications.waset.org/abstracts/135144/the-profit-trend-of-cosmetics-products-using-bootstrap-edgeworth-approximation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/135144.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">159</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">497</span> High Accuracy Analytic Approximation for Special Functions Applied to Bessel Functions J₀(x) and Its Zeros</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fernando%20Maass">Fernando Maass</a>, <a href="https://publications.waset.org/abstracts/search?q=Pablo%20Martin"> Pablo Martin</a>, <a href="https://publications.waset.org/abstracts/search?q=Jorge%20Olivares"> Jorge Olivares</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Bessel function J₀(x) is very important in Electrodynamics and Physics, as well as its zeros. In this work, a method to obtain high accuracy approximation is presented through an application to that function. In most of the applications of this function, the values of the zeros are very important. In this work, analytic approximations for this function have been obtained valid for all positive values of the variable x, which have high accuracy for the function as well as for the zeros. The approximation is determined by the simultaneous used of the power series and asymptotic expansion. The structure of the approximation is a combination of two rational functions with elementary functions as trigonometric and fractional powers. Here us in Pade method, rational functions are used, but now there combined with elementary functions us fractional powers hyperbolic or trigonometric functions, and others. The reason of this is that now power series of the exact function are used, but together with the asymptotic expansion, which usually includes fractional powers trigonometric functions and other type of elementary functions. The approximation must be a bridge between both expansions, and this can not be accomplished using only with rational functions. In the simplest approximation using 4 parameters the maximum absolute error is less than 0.006 at x ∼ 4.9. In this case also the maximum relative error for the zeros is less than 0.003 which is for the second zero, but that value decreases rapidly for the other zeros. The same kind of behaviour happens for the relative error of the maximum and minimum of the functions. Approximations with higher accuracy and more parameters will be also shown. All the approximations are valid for any positive value of x, and they can be calculated easily. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=analytic%20approximations" title="analytic approximations">analytic approximations</a>, <a href="https://publications.waset.org/abstracts/search?q=asymptotic%20approximations" title=" asymptotic approximations"> asymptotic approximations</a>, <a href="https://publications.waset.org/abstracts/search?q=Bessel%20functions" title=" Bessel functions"> Bessel functions</a>, <a href="https://publications.waset.org/abstracts/search?q=quasirational%20approximations" title=" quasirational approximations"> quasirational approximations</a> </p> <a href="https://publications.waset.org/abstracts/92867/high-accuracy-analytic-approximation-for-special-functions-applied-to-bessel-functions-j0x-and-its-zeros" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/92867.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">251</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">496</span> Improved Pitch Detection Using Fourier Approximation Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Balachandra%20Kumaraswamy">Balachandra Kumaraswamy</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20G.%20Poonacha"> P. G. Poonacha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Automatic Music Information Retrieval has been one of the challenging topics of research for a few decades now with several interesting approaches reported in the literature. In this paper we have developed a pitch extraction method based on a finite Fourier series approximation to the given window of samples. We then estimate pitch as the fundamental period of the finite Fourier series approximation to the given window of samples. This method uses analysis of the strength of harmonics present in the signal to reduce octave as well as harmonic errors. The performance of our method is compared with three best known methods for pitch extraction, namely, Yin, Windowed Special Normalization of the Auto-Correlation Function and Harmonic Product Spectrum methods of pitch extraction. Our study with artificially created signals as well as music files show that Fourier Approximation method gives much better estimate of pitch with less octave and harmonic errors. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=pitch" title="pitch">pitch</a>, <a href="https://publications.waset.org/abstracts/search?q=fourier%20series" title=" fourier series"> fourier series</a>, <a href="https://publications.waset.org/abstracts/search?q=yin" title=" yin"> yin</a>, <a href="https://publications.waset.org/abstracts/search?q=normalization%20of%20the%20auto-%20correlation%20function" title=" normalization of the auto- correlation function"> normalization of the auto- correlation function</a>, <a href="https://publications.waset.org/abstracts/search?q=harmonic%20product" title=" harmonic product"> harmonic product</a>, <a href="https://publications.waset.org/abstracts/search?q=mean%20square%20error" title=" mean square error"> mean square error</a> </p> <a href="https://publications.waset.org/abstracts/36472/improved-pitch-detection-using-fourier-approximation-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36472.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">412</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">495</span> Particle Swarm Optimization and Quantum Particle Swarm Optimization to Multidimensional Function Approximation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Diogo%20Silva">Diogo Silva</a>, <a href="https://publications.waset.org/abstracts/search?q=Fadul%20Rodor"> Fadul Rodor</a>, <a href="https://publications.waset.org/abstracts/search?q=Carlos%20Moraes"> Carlos Moraes</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work compares the results of multidimensional function approximation using two algorithms: the classical Particle Swarm Optimization (PSO) and the Quantum Particle Swarm Optimization (QPSO). These algorithms were both tested on three functions - The Rosenbrock, the Rastrigin, and the sphere functions - with different characteristics by increasing their number of dimensions. As a result, this study shows that the higher the function space, i.e. the larger the function dimension, the more evident the advantages of using the QPSO method compared to the PSO method in terms of performance and number of necessary iterations to reach the stop criterion. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=PSO" title="PSO">PSO</a>, <a href="https://publications.waset.org/abstracts/search?q=QPSO" title=" QPSO"> QPSO</a>, <a href="https://publications.waset.org/abstracts/search?q=function%20approximation" title=" function approximation"> function approximation</a>, <a href="https://publications.waset.org/abstracts/search?q=AI" title=" AI"> AI</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=multidimensional%20functions" title=" multidimensional functions"> multidimensional functions</a> </p> <a href="https://publications.waset.org/abstracts/81790/particle-swarm-optimization-and-quantum-particle-swarm-optimization-to-multidimensional-function-approximation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/81790.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">589</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">494</span> The Modelling of Real Time Series Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Valeria%20Bondarenko">Valeria Bondarenko</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We proposed algorithms for: estimation of parameters fBm (volatility and Hurst exponent) and for the approximation of random time series by functional of fBm. We proved the consistency of the estimators, which constitute the above algorithms, and proved the optimal forecast of approximated time series. The adequacy of estimation algorithms, approximation, and forecasting is proved by numerical experiment. During the process of creating software, the system has been created, which is displayed by the hierarchical structure. The comparative analysis of proposed algorithms with the other methods gives evidence of the advantage of approximation method. The results can be used to develop methods for the analysis and modeling of time series describing the economic, physical, biological and other processes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mathematical%20model" title="mathematical model">mathematical model</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20process" title=" random process"> random process</a>, <a href="https://publications.waset.org/abstracts/search?q=Wiener%20process" title=" Wiener process"> Wiener process</a>, <a href="https://publications.waset.org/abstracts/search?q=fractional%20Brownian%20motion" title=" fractional Brownian motion"> fractional Brownian motion</a> </p> <a href="https://publications.waset.org/abstracts/49210/the-modelling-of-real-time-series-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49210.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">358</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">493</span> Localising Gauss’s Law and the Electric Charge Induction on a Conducting Sphere</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sirapat%20Lookrak">Sirapat Lookrak</a>, <a href="https://publications.waset.org/abstracts/search?q=Anol%20Paisal"> Anol Paisal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Space debris has numerous manifestations, including ferro-metalize and non-ferrous. The electric field will induce negative charges to split from positive charges inside the space debris. In this research, we focus only on conducting materials. The assumption is that the electric charge density of a conducting surface is proportional to the electric field on that surface due to Gauss's Law. We are trying to find the induced charge density from an external electric field perpendicular to a conducting spherical surface. An object is a sphere on which the external electric field is not uniform. The electric field is, therefore, considered locally. The localised spherical surface is a tangent plane, so the Gaussian surface is a very small cylinder, and every point on a spherical surface has its own cylinder. The electric field from a circular electrode has been calculated in near-field and far-field approximation and shown Explanation Touchless maneuvering space debris orbit properties. The electric charge density calculation from a near-field and far-field approximation is done. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=near-field%20approximation" title="near-field approximation">near-field approximation</a>, <a href="https://publications.waset.org/abstracts/search?q=far-field%20approximation" title=" far-field approximation"> far-field approximation</a>, <a href="https://publications.waset.org/abstracts/search?q=localized%20Gauss%E2%80%99s%20law" title=" localized Gauss’s law"> localized Gauss’s law</a>, <a href="https://publications.waset.org/abstracts/search?q=electric%20charge%20density" title=" electric charge density"> electric charge density</a> </p> <a href="https://publications.waset.org/abstracts/150159/localising-gausss-law-and-the-electric-charge-induction-on-a-conducting-sphere" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150159.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">132</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">492</span> Approximation by Generalized Lupaş-Durrmeyer Operators with Two Parameter α and β</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Preeti%20Sharma">Preeti Sharma</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper deals with the Stancu type generalization of Lupaş-Durrmeyer operators. We establish some direct results in the polynomial weighted space of continuous functions defined on the interval [0, 1]. Also, Voronovskaja type theorem is studied. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lupas-Durrmeyer%20operators" title="Lupas-Durrmeyer operators">Lupas-Durrmeyer operators</a>, <a href="https://publications.waset.org/abstracts/search?q=polya%20distribution" title=" polya distribution"> polya distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=weighted%20approximation" title=" weighted approximation"> weighted approximation</a>, <a href="https://publications.waset.org/abstracts/search?q=rate%20of%20convergence" title=" rate of convergence"> rate of convergence</a>, <a href="https://publications.waset.org/abstracts/search?q=modulus%20of%20continuity" title=" modulus of continuity"> modulus of continuity</a> </p> <a href="https://publications.waset.org/abstracts/47660/approximation-by-generalized-lupas-durrmeyer-operators-with-two-parameter-a-and-v" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/47660.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">346</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">491</span> Ab Initio Calculation of Fundamental Properties of CaxMg1-xA (a = Se and Te) Alloys in the Rock-Salt Structure</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20A.%20Ghebouli">M. A. Ghebouli</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20Choutri"> H. Choutri</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Ghebouli"> B. Ghebouli </a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Fatmi"> M. Fatmi</a>, <a href="https://publications.waset.org/abstracts/search?q=L.%20Louail"> L. Louail</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We employed the density-functional perturbation theory (DFPT) within the generalized gradient approximation (GGA), the local density approximation (LDA) and the virtual-crystal approximation (VCA) to study the effect of composition on the structure, stability, energy gaps, electron effective mass, the dynamic effective charge, optical and acoustical phonon frequencies and static and high dielectric constants of the rock-salt CaxMg1-xSe and CaxMg1-xTe alloys. The computed equilibrium lattice constant and bulk modulus show an important deviation from the linear concentration. From the Voigt-Reuss-Hill approximation, CaxMg1-xSe and CaxMg1-xTe present lower stiffness and lateral expansion. For Ca content ranging between 0.25-0.75, the elastic constants, energy gaps, electron effective mass and dynamic effective charge are predictions. The elastic constants and computed phonon dispersion curves indicate that these alloys are mechanically stable. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CaxMg1-xSe" title="CaxMg1-xSe">CaxMg1-xSe</a>, <a href="https://publications.waset.org/abstracts/search?q=CaxMg1-xTe" title=" CaxMg1-xTe"> CaxMg1-xTe</a>, <a href="https://publications.waset.org/abstracts/search?q=band%20structure" title=" band structure"> band structure</a>, <a href="https://publications.waset.org/abstracts/search?q=phonon" title=" phonon"> phonon</a> </p> <a href="https://publications.waset.org/abstracts/13861/ab-initio-calculation-of-fundamental-properties-of-caxmg1-xa-a-se-and-te-alloys-in-the-rock-salt-structure" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13861.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">540</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">490</span> Bayesian Analysis of Topp-Leone Generalized Exponential Distribution</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Najrullah%20Khan">Najrullah Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=Athar%20Ali%20Khan"> Athar Ali Khan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Topp-Leone distribution was introduced by Topp- Leone in 1955. In this paper, an attempt has been made to fit Topp-Leone Generalized exponential (TPGE) distribution. A real survival data set is used for illustrations. Implementation is done using R and JAGS and appropriate illustrations are made. R and JAGS codes have been provided to implement censoring mechanism using both optimization and simulation tools. The main aim of this paper is to describe and illustrate the Bayesian modelling approach to the analysis of survival data. Emphasis is placed on the modeling of data and the interpretation of the results. Crucial to this is an understanding of the nature of the incomplete or 'censored' data encountered. Analytic approximation and simulation tools are covered here, but most of the emphasis is on Markov chain based Monte Carlo method including independent Metropolis algorithm, which is currently the most popular technique. For analytic approximation, among various optimization algorithms and trust region method is found to be the best. In this paper, TPGE model is also used to analyze the lifetime data in Bayesian paradigm. Results are evaluated from the above mentioned real survival data set. The analytic approximation and simulation methods are implemented using some software packages. It is clear from our findings that simulation tools provide better results as compared to those obtained by asymptotic approximation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20Inference" title="Bayesian Inference">Bayesian Inference</a>, <a href="https://publications.waset.org/abstracts/search?q=JAGS" title=" JAGS"> JAGS</a>, <a href="https://publications.waset.org/abstracts/search?q=Laplace%20Approximation" title=" Laplace Approximation"> Laplace Approximation</a>, <a href="https://publications.waset.org/abstracts/search?q=LaplacesDemon" title=" LaplacesDemon"> LaplacesDemon</a>, <a href="https://publications.waset.org/abstracts/search?q=posterior" title=" posterior"> posterior</a>, <a href="https://publications.waset.org/abstracts/search?q=R%20Software" title=" R Software"> R Software</a>, <a href="https://publications.waset.org/abstracts/search?q=simulation" title=" simulation"> simulation</a> </p> <a href="https://publications.waset.org/abstracts/77532/bayesian-analysis-of-topp-leone-generalized-exponential-distribution" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77532.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">535</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">489</span> Approximation Algorithms for Peak-Demand Reduction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zaid%20Jamal%20Saeed%20Almahmoud">Zaid Jamal Saeed Almahmoud</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Smart grid is emerging as the future power grid, with smart techniques to optimize power consumption and electricity generation. Minimizing peak power consumption under a fixed delay requirement is a significant problem in the smart grid.For this problem, all appliances must be scheduled within a given finite time duration. We consider the problem of minimizing the peak demand under appliances constraints by scheduling power jobs with uniform release dates and deadlines. As the problem is known to be NP-hard, we analyze the performance of a version of the natural greedy heuristic for solving this problem. Our theoretical analysis and experimental results show that the proposed heuristic outperforms existing methods by providing a better approximation to the optimal solution. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=peak%20demand%20scheduling" title="peak demand scheduling">peak demand scheduling</a>, <a href="https://publications.waset.org/abstracts/search?q=approximation%20algorithms" title=" approximation algorithms"> approximation algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=smart%20grid" title=" smart grid"> smart grid</a>, <a href="https://publications.waset.org/abstracts/search?q=heuristics" title=" heuristics"> heuristics</a> </p> <a href="https://publications.waset.org/abstracts/157964/approximation-algorithms-for-peak-demand-reduction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/157964.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">94</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">488</span> Identification of Wiener Model Using Iterative Schemes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vikram%20Saini">Vikram Saini</a>, <a href="https://publications.waset.org/abstracts/search?q=Lillie%20Dewan"> Lillie Dewan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents the iterative schemes based on Least square, Hierarchical Least Square and Stochastic Approximation Gradient method for the Identification of Wiener model with parametric structure. A gradient method is presented for the parameter estimation of wiener model with noise conditions based on the stochastic approximation. Simulation results are presented for the Wiener model structure with different static non-linear elements in the presence of colored noise to show the comparative analysis of the iterative methods. The stochastic gradient method shows improvement in the estimation performance and provides fast convergence of the parameters estimates. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hard%20non-linearity" title="hard non-linearity">hard non-linearity</a>, <a href="https://publications.waset.org/abstracts/search?q=least%20square" title=" least square"> least square</a>, <a href="https://publications.waset.org/abstracts/search?q=parameter%20estimation" title=" parameter estimation"> parameter estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20approximation%20gradient" title=" stochastic approximation gradient"> stochastic approximation gradient</a>, <a href="https://publications.waset.org/abstracts/search?q=Wiener%20model" title=" Wiener model"> Wiener model</a> </p> <a href="https://publications.waset.org/abstracts/70632/identification-of-wiener-model-using-iterative-schemes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/70632.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">405</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=saddlepoint%20approximation&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=saddlepoint%20approximation&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=saddlepoint%20approximation&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=saddlepoint%20approximation&page=5">5</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=saddlepoint%20approximation&page=6">6</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=saddlepoint%20approximation&page=7">7</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=saddlepoint%20approximation&page=8">8</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=saddlepoint%20approximation&page=9">9</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=saddlepoint%20approximation&page=10">10</a></li> <li class="page-item disabled"><span class="page-link">...</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=saddlepoint%20approximation&page=17">17</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=saddlepoint%20approximation&page=18">18</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=saddlepoint%20approximation&page=2" rel="next">&rsaquo;</a></li> </ul> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/WASET-16th-foundational-anniversary.pdf">WASET celebrates its 16th foundational anniversary</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Account <li><a href="https://waset.org/profile">My Account</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Explore <li><a href="https://waset.org/disciplines">Disciplines</a></li> <li><a href="https://waset.org/conferences">Conferences</a></li> <li><a href="https://waset.org/conference-programs">Conference Program</a></li> <li><a href="https://waset.org/committees">Committees</a></li> <li><a href="https://publications.waset.org">Publications</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Research <li><a href="https://publications.waset.org/abstracts">Abstracts</a></li> <li><a href="https://publications.waset.org">Periodicals</a></li> <li><a href="https://publications.waset.org/archive">Archive</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Open Science <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Philosophy.pdf">Open Science Philosophy</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Award.pdf">Open Science Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Society-Open-Science-and-Open-Innovation.pdf">Open Innovation</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Postdoctoral-Fellowship-Award.pdf">Postdoctoral Fellowship Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Scholarly-Research-Review.pdf">Scholarly Research Review</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Support <li><a href="https://waset.org/page/support">Support</a></li> <li><a href="https://waset.org/profile/messages/create">Contact Us</a></li> <li><a href="https://waset.org/profile/messages/create">Report Abuse</a></li> </ul> </div> </div> </div> </div> </div> <div class="container text-center"> <hr style="margin-top:0;margin-bottom:.3rem;"> <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank" class="text-muted small">Creative Commons Attribution 4.0 International License</a> <div id="copy" class="mt-2">© 2024 World Academy of Science, Engineering and Technology</div> </div> </footer> <a href="javascript:" id="return-to-top"><i class="fas fa-arrow-up"></i></a> <div class="modal" id="modal-template"> <div class="modal-dialog"> <div class="modal-content"> <div class="row m-0 mt-1"> <div class="col-md-12"> <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">×</span></button> </div> </div> <div class="modal-body"></div> </div> </div> </div> <script src="https://cdn.waset.org/static/plugins/jquery-3.3.1.min.js"></script> <script src="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/js/bootstrap.bundle.min.js"></script> <script src="https://cdn.waset.org/static/js/site.js?v=150220211556"></script> <script> jQuery(document).ready(function() { /*jQuery.get("https://publications.waset.org/xhr/user-menu", function (response) { jQuery('#mainNavMenu').append(response); });*/ jQuery.get({ url: "https://publications.waset.org/xhr/user-menu", cache: false }).then(function(response){ jQuery('#mainNavMenu').append(response); }); }); </script> </body> </html>