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estimators</title> <meta name="description" content="Search results for: generalized estimators"> <meta name="keywords" content="generalized estimators"> <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 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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="generalized estimators"> <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> 852</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: generalized estimators</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">852</span> Some Generalized Multivariate Estimators for Population Mean under Multi Phase Stratified Systematic Sampling</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muqaddas%20Javed">Muqaddas Javed</a>, <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Hanif"> Muhammad Hanif</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The generalized multivariate ratio and regression type estimators for population mean are suggested under multi-phase stratified systematic sampling (MPSSS) using multi auxiliary information. Estimators are developed under the two different situations of availability of auxiliary information. The expressions of bias and mean square error (MSE) are developed. Special cases of suggested estimators are also discussed and simulation study is conducted to observe the performance of estimators. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=generalized%20estimators" title="generalized estimators">generalized estimators</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-phase%20sampling" title=" multi-phase sampling"> multi-phase sampling</a>, <a href="https://publications.waset.org/abstracts/search?q=stratified%20random%20sampling" title=" stratified random sampling"> stratified random sampling</a>, <a href="https://publications.waset.org/abstracts/search?q=systematic%20sampling" title=" systematic sampling"> systematic sampling</a> </p> <a href="https://publications.waset.org/abstracts/27296/some-generalized-multivariate-estimators-for-population-mean-under-multi-phase-stratified-systematic-sampling" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27296.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">728</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">851</span> Point Estimation for the Type II Generalized Logistic Distribution Based on Progressively Censored Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rana%20Rimawi">Rana Rimawi</a>, <a href="https://publications.waset.org/abstracts/search?q=Ayman%20Baklizi"> Ayman Baklizi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Skewed distributions are important models that are frequently used in applications. Generalized distributions form a class of skewed distributions and gain widespread use in applications because of their flexibility in data analysis. More specifically, the Generalized Logistic Distribution with its different types has received considerable attention recently. In this study, based on progressively type-II censored data, we will consider point estimation in type II Generalized Logistic Distribution (Type II GLD). We will develop several estimators for its unknown parameters, including maximum likelihood estimators (MLE), Bayes estimators and linear estimators (BLUE). The estimators will be compared using simulation based on the criteria of bias and Mean square error (MSE). An illustrative example of a real data set will be given. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=point%20estimation" title="point estimation">point estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=type%20II%20generalized%20logistic%20distribution" title=" type II generalized logistic distribution"> type II generalized logistic distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=progressive%20censoring" title=" progressive censoring"> progressive censoring</a>, <a href="https://publications.waset.org/abstracts/search?q=maximum%20likelihood%20estimation" title=" maximum likelihood estimation"> maximum likelihood estimation</a> </p> <a href="https://publications.waset.org/abstracts/142979/point-estimation-for-the-type-ii-generalized-logistic-distribution-based-on-progressively-censored-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/142979.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">197</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">850</span> Comparative Study of Estimators of Population Means in Two Phase Sampling in the Presence of Non-Response</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Syed%20Ali%20Taqi">Syed Ali Taqi</a>, <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Ismail"> Muhammad Ismail</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A comparative study of estimators of population means in two phase sampling in the presence of non-response when Unknown population means of the auxiliary variable(s) and incomplete information of study variable y as well as of auxiliary variable(s) is made. Three real data sets of University students, hospital and unemployment are used for comparison of all the available techniques in two phase sampling in the presence of non-response with the newly generalized ratio estimators. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=two-phase%20sampling" title="two-phase sampling">two-phase sampling</a>, <a href="https://publications.waset.org/abstracts/search?q=ratio%20estimator" title=" ratio estimator"> ratio estimator</a>, <a href="https://publications.waset.org/abstracts/search?q=product%20estimator" title=" product estimator"> product estimator</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20estimators" title=" generalized estimators"> generalized estimators</a> </p> <a href="https://publications.waset.org/abstracts/79636/comparative-study-of-estimators-of-population-means-in-two-phase-sampling-in-the-presence-of-non-response" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/79636.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">233</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">849</span> A Generalized Family of Estimators for Estimation of Unknown Population Variance in Simple Random Sampling</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Saba%20Riaz">Saba Riaz</a>, <a href="https://publications.waset.org/abstracts/search?q=Syed%20A.%20Hussain"> Syed A. Hussain</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper is addressing the estimation method of the unknown population variance of the variable of interest. A new generalized class of estimators of the finite population variance has been suggested using the auxiliary information. To improve the precision of the proposed class, known population variance of the auxiliary variable has been used. Mathematical expressions for the biases and the asymptotic variances of the suggested class are derived under large sample approximation. Theoretical and numerical comparisons are made to investigate the performances of the proposed class of estimators. The empirical study reveals that the suggested class of estimators performs better than the usual estimator, classical ratio estimator, classical product estimator and classical linear regression estimator. It has also been found that the suggested class of estimators is also more efficient than some recently published estimators. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=study%20variable" title="study variable">study variable</a>, <a href="https://publications.waset.org/abstracts/search?q=auxiliary%20variable" title=" auxiliary variable"> auxiliary variable</a>, <a href="https://publications.waset.org/abstracts/search?q=finite%20population%20variance" title=" finite population variance"> finite population variance</a>, <a href="https://publications.waset.org/abstracts/search?q=bias" title=" bias"> bias</a>, <a href="https://publications.waset.org/abstracts/search?q=asymptotic%20variance" title=" asymptotic variance"> asymptotic variance</a>, <a href="https://publications.waset.org/abstracts/search?q=percent%20relative%20efficiency" title=" percent relative efficiency"> percent relative efficiency</a> </p> <a href="https://publications.waset.org/abstracts/87115/a-generalized-family-of-estimators-for-estimation-of-unknown-population-variance-in-simple-random-sampling" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/87115.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">225</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">848</span> Parameter Estimation for the Mixture of Generalized Gamma Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wikanda%20Phaphan">Wikanda Phaphan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Mixture generalized gamma distribution is a combination of two distributions: generalized gamma distribution and length biased generalized gamma distribution. These two distributions were presented by Suksaengrakcharoen and Bodhisuwan in 2014. The findings showed that probability density function (pdf) had fairly complexities, so it made problems in estimating parameters. The problem occurred in parameter estimation was that we were unable to calculate estimators in the form of critical expression. Thus, we will use numerical estimation to find the estimators. In this study, we presented a new method of the parameter estimation by using the expectation – maximization algorithm (EM), the conjugate gradient method, and the quasi-Newton method. The data was generated by acceptance-rejection method which is used for estimating α, β, λ and p. λ is the scale parameter, p is the weight parameter, α and β are the shape parameters. We will use Monte Carlo technique to find the estimator's performance. Determining the size of sample equals 10, 30, 100; the simulations were repeated 20 times in each case. We evaluated the effectiveness of the estimators which was introduced by considering values of the mean squared errors and the bias. The findings revealed that the EM-algorithm had proximity to the actual values determined. Also, the maximum likelihood estimators via the conjugate gradient and the quasi-Newton method are less precision than the maximum likelihood estimators via the EM-algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=conjugate%20gradient%20method" title="conjugate gradient method">conjugate gradient method</a>, <a href="https://publications.waset.org/abstracts/search?q=quasi-Newton%20method" title=" quasi-Newton method"> quasi-Newton method</a>, <a href="https://publications.waset.org/abstracts/search?q=EM-algorithm" title=" EM-algorithm"> EM-algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20gamma%20distribution" title=" generalized gamma distribution"> generalized gamma distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=length%20biased%20generalized%20gamma%20distribution" title=" length biased generalized gamma distribution"> length biased generalized gamma distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=maximum%20likelihood%20method" title=" maximum likelihood method"> maximum likelihood method</a> </p> <a href="https://publications.waset.org/abstracts/81404/parameter-estimation-for-the-mixture-of-generalized-gamma-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/81404.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">219</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">847</span> Estimation of Population Mean Using Characteristics of Poisson Distribution: An Application to Earthquake Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Prayas%20Sharma">Prayas Sharma</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper proposed a generalized class of estimators, an exponential class of estimators based on the adaption of Sharma and Singh (2015) and Solanki and Singh (2013), and a simple difference estimator for estimating unknown population mean in the case of Poisson distributed population in simple random sampling without replacement. The expressions for mean square errors of the proposed classes of estimators are derived from the first order of approximation. It is shown that the adapted version of Solanki and Singh (2013), the exponential class of estimator, is always more efficient than the usual estimator, ratio, product, exponential ratio, and exponential product type estimators and equally efficient to simple difference estimator. Moreover, the adapted version of Sharma and Singh's (2015) estimator is always more efficient than all the estimators available in the literature. In addition, theoretical findings are supported by an empirical study to show the superiority of the constructed estimators over others with an application to earthquake data of Turkey. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=auxiliary%20attribute" title="auxiliary attribute">auxiliary attribute</a>, <a href="https://publications.waset.org/abstracts/search?q=point%20bi-serial" title=" point bi-serial"> point bi-serial</a>, <a href="https://publications.waset.org/abstracts/search?q=mean%20square%20error" title=" mean square error"> mean square error</a>, <a href="https://publications.waset.org/abstracts/search?q=simple%20random%20sampling" title=" simple random sampling"> simple random sampling</a>, <a href="https://publications.waset.org/abstracts/search?q=Poisson%20distribution" title=" Poisson distribution"> Poisson distribution</a> </p> <a href="https://publications.waset.org/abstracts/171049/estimation-of-population-mean-using-characteristics-of-poisson-distribution-an-application-to-earthquake-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/171049.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">155</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">846</span> Alternative Robust Estimators for the Shape Parameters of the Burr XII Distribution</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fatma%20Zehra%20Do%C4%9Fru">Fatma Zehra Doğru</a>, <a href="https://publications.waset.org/abstracts/search?q=Olcay%20Arslan"> Olcay Arslan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we propose alternative robust estimators for the shape parameters of the Burr XII distribution. We provide a small simulation study and a real data example to illustrate the performance of the proposed estimators over the ML and the LS estimators. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=burr%20xii%20distribution" title="burr xii distribution">burr xii distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=robust%20estimator" title=" robust estimator"> robust estimator</a>, <a href="https://publications.waset.org/abstracts/search?q=m-estimator" title=" m-estimator"> m-estimator</a>, <a href="https://publications.waset.org/abstracts/search?q=least%20squares" title=" least squares"> least squares</a> </p> <a href="https://publications.waset.org/abstracts/30038/alternative-robust-estimators-for-the-shape-parameters-of-the-burr-xii-distribution" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/30038.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">428</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">845</span> Improved Estimation Strategies of Sensitive Characteristics Using Scrambled Response Techniques in Successive Sampling</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Suman">S. Suman</a>, <a href="https://publications.waset.org/abstracts/search?q=G.%20N.%20Singh"> G. N. Singh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This research work is an effort to analyse the consequences of scrambled response technique to estimate the current population mean in two-occasion successive sampling when the characteristic of interest is sensitive in nature. The generalized estimation procedures have been proposed using sensitive auxiliary variables under additive and multiplicative scramble models. The properties of resultant estimators have been deeply examined. Simulation, as well as empirical studies, are carried out to evaluate the performances of the proposed estimators with respect to other competent estimators. The results of our studies suggest that the proposed estimation procedures are highly effective under the presence of non-response situation. The result of this study also suggests that additive scrambled response model is a better choice in the perspective of cost of the survey and privacy of the respondents. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=scrambled%20response" title="scrambled response">scrambled response</a>, <a href="https://publications.waset.org/abstracts/search?q=sensitive%20characteristic" title=" sensitive characteristic"> sensitive characteristic</a>, <a href="https://publications.waset.org/abstracts/search?q=successive%20sampling" title=" successive sampling"> successive sampling</a>, <a href="https://publications.waset.org/abstracts/search?q=optimum%20replacement%20strategy" title=" optimum replacement strategy"> optimum replacement strategy</a> </p> <a href="https://publications.waset.org/abstracts/95355/improved-estimation-strategies-of-sensitive-characteristics-using-scrambled-response-techniques-in-successive-sampling" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/95355.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">177</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">844</span> The Generalized Pareto Distribution as a Model for Sequential Order Statistics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mahdy%20%E2%80%8EEsmailian">Mahdy ‎Esmailian</a>, <a href="https://publications.waset.org/abstracts/search?q=Mahdi%20%E2%80%8EDoostparast"> Mahdi ‎Doostparast</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmad%20%E2%80%8EParsian"> Ahmad ‎Parsian</a> </p> <p class="card-text"><strong>Abstract:</strong></p> ‎In this article‎, ‎sequential order statistics (SOS) censoring type II samples coming from the generalized Pareto distribution are considered‎. ‎Maximum likelihood (ML) estimators of the unknown parameters are derived on the basis of the available multiple SOS data‎. ‎Necessary conditions for existence and uniqueness of the derived ML estimates are given‎. Due to complexity in the proposed likelihood function‎, ‎a useful re-parametrization is suggested‎. ‎For illustrative purposes‎, ‎a Monte Carlo simulation study is conducted and an illustrative example is analysed‎. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bayesian%20estimation%E2%80%8E" title="bayesian estimation‎">bayesian estimation‎</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20pareto%20distribution%E2%80%8E" title=" generalized pareto distribution‎"> generalized pareto distribution‎</a>, <a href="https://publications.waset.org/abstracts/search?q=%E2%80%8Emaximum%20likelihood%20%20estimation%E2%80%8E" title=" ‎maximum likelihood estimation‎"> ‎maximum likelihood estimation‎</a>, <a href="https://publications.waset.org/abstracts/search?q=sequential%20order%20statistics" title=" sequential order statistics"> sequential order statistics</a> </p> <a href="https://publications.waset.org/abstracts/26988/the-generalized-pareto-distribution-as-a-model-for-sequential-order-statistics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/26988.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">509</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">843</span> A Bivariate Inverse Generalized Exponential Distribution and Its Applications in Dependent Competing Risks Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fatemah%20A.%20Alqallaf">Fatemah A. Alqallaf</a>, <a href="https://publications.waset.org/abstracts/search?q=Debasis%20Kundu"> Debasis Kundu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The aim of this paper is to introduce a bivariate inverse generalized exponential distribution which has a singular component. The proposed bivariate distribution can be used when the marginals have heavy-tailed distributions, and they have non-monotone hazard functions. Due to the presence of the singular component, it can be used quite effectively when there are ties in the data. Since it has four parameters, it is a very flexible bivariate distribution, and it can be used quite effectively for analyzing various bivariate data sets. Several dependency properties and dependency measures have been obtained. The maximum likelihood estimators cannot be obtained in closed form, and it involves solving a four-dimensional optimization problem. To avoid that, we have proposed to use an EM algorithm, and it involves solving only one non-linear equation at each `E'-step. Hence, the implementation of the proposed EM algorithm is very straight forward in practice. Extensive simulation experiments and the analysis of one data set have been performed. We have observed that the proposed bivariate inverse generalized exponential distribution can be used for modeling dependent competing risks data. One data set has been analyzed to show the effectiveness of the proposed model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Block%20and%20Basu%20bivariate%20distributions" title="Block and Basu bivariate distributions">Block and Basu bivariate distributions</a>, <a href="https://publications.waset.org/abstracts/search?q=competing%20risks" title=" competing risks"> competing risks</a>, <a href="https://publications.waset.org/abstracts/search?q=EM%20algorithm" title=" EM algorithm"> EM algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=Marshall-Olkin%20bivariate%20exponential%20distribution" title=" Marshall-Olkin bivariate exponential distribution"> Marshall-Olkin bivariate exponential distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=maximum%20likelihood%20estimators" title=" maximum likelihood estimators"> maximum likelihood estimators</a> </p> <a href="https://publications.waset.org/abstracts/122524/a-bivariate-inverse-generalized-exponential-distribution-and-its-applications-in-dependent-competing-risks-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/122524.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">143</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">842</span> Estimation of Rare and Clustered Population Mean Using Two Auxiliary Variables in Adaptive Cluster Sampling</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Nouman%20Qureshi">Muhammad Nouman Qureshi</a>, <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Hanif"> Muhammad Hanif</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Adaptive cluster sampling (ACS) is specifically developed for the estimation of highly clumped populations and applied to a wide range of situations like animals of rare and endangered species, uneven minerals, HIV patients and drug users. In this paper, we proposed a generalized semi-exponential estimator with two auxiliary variables under the framework of ACS design. The expressions of approximate bias and mean square error (MSE) of the proposed estimator are derived. Theoretical comparisons of the proposed estimator have been made with existing estimators. A numerical study is conducted on real and artificial populations to demonstrate and compare the efficiencies of the proposed estimator. The results indicate that the proposed generalized semi-exponential estimator performed considerably better than all the adaptive and non-adaptive estimators considered in this paper. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=auxiliary%20information" title="auxiliary information">auxiliary information</a>, <a href="https://publications.waset.org/abstracts/search?q=adaptive%20cluster%20sampling" title=" adaptive cluster sampling"> adaptive cluster sampling</a>, <a href="https://publications.waset.org/abstracts/search?q=clustered%20populations" title=" clustered populations"> clustered populations</a>, <a href="https://publications.waset.org/abstracts/search?q=Hansen-Hurwitz%20estimation" title=" Hansen-Hurwitz estimation"> Hansen-Hurwitz estimation</a> </p> <a href="https://publications.waset.org/abstracts/98047/estimation-of-rare-and-clustered-population-mean-using-two-auxiliary-variables-in-adaptive-cluster-sampling" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/98047.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">238</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">841</span> Proficient Estimation Procedure for a Rare Sensitive Attribute Using Poisson Distribution</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Suman">S. Suman</a>, <a href="https://publications.waset.org/abstracts/search?q=G.%20N.%20Singh"> G. N. Singh </a> </p> <p class="card-text"><strong>Abstract:</strong></p> The present manuscript addresses the estimation procedure of population parameter using Poisson probability distribution when characteristic under study possesses a rare sensitive attribute. The generalized form of unrelated randomized response model is suggested in order to acquire the truthful responses from respondents. The resultant estimators have been proposed for two situations when the information on an unrelated rare non-sensitive characteristic is known as well as unknown. The properties of the proposed estimators are derived, and the measure of confidentiality of respondent is also suggested for respondents. Empirical studies are carried out in the support of discussed theory. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Poisson%20distribution" title="Poisson distribution">Poisson distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=randomized%20response%20model" title=" randomized response model"> randomized response model</a>, <a href="https://publications.waset.org/abstracts/search?q=rare%20sensitive%20attribute" title=" rare sensitive attribute"> rare sensitive attribute</a>, <a href="https://publications.waset.org/abstracts/search?q=non-sensitive%20attribute" title=" non-sensitive attribute"> non-sensitive attribute</a> </p> <a href="https://publications.waset.org/abstracts/95219/proficient-estimation-procedure-for-a-rare-sensitive-attribute-using-poisson-distribution" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/95219.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">266</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">840</span> Population Size Estimation Based on the GPD</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=O.%20Anan">O. Anan</a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20B%C3%B6hning"> D. Böhning</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Maruotti"> A. Maruotti</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The purpose of the study is to estimate the elusive target population size under a truncated count model that accounts for heterogeneity. The purposed estimator is based on the generalized Poisson distribution (GPD), which extends the Poisson distribution by adding a dispersion parameter. Thus, it becomes an useful model for capture-recapture data where concurrent events are not homogeneous. In addition, it can account for over-dispersion and under-dispersion. The ratios of neighboring frequency counts are used as a tool for investigating the validity of whether generalized Poisson or Poisson distribution. Since capture-recapture approaches do not provide the zero counts, the estimated parameters can be achieved by modifying the EM-algorithm technique for the zero-truncated generalized Poisson distribution. The properties and the comparative performance of proposed estimator were investigated through simulation studies. Furthermore, some empirical examples are represented insights on the behavior of the estimators. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=capture" title="capture">capture</a>, <a href="https://publications.waset.org/abstracts/search?q=recapture%20methods" title=" recapture methods"> recapture methods</a>, <a href="https://publications.waset.org/abstracts/search?q=ratio%20plot" title=" ratio plot"> ratio plot</a>, <a href="https://publications.waset.org/abstracts/search?q=heterogeneous%20population" title=" heterogeneous population"> heterogeneous population</a>, <a href="https://publications.waset.org/abstracts/search?q=zero-truncated%20count" title=" zero-truncated count"> zero-truncated count</a> </p> <a href="https://publications.waset.org/abstracts/37160/population-size-estimation-based-on-the-gpd" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/37160.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">435</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">839</span> Estimation of Population Mean under Random Non-Response in Two-Phase Successive Sampling</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Khalid">M. Khalid</a>, <a href="https://publications.waset.org/abstracts/search?q=G.%20N.%20Singh"> G. N. Singh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we have considered the problem of estimation for population mean, on current (second) occasion in the presence of random non response in two-occasion successive sampling under two phase set-up. Modified exponential type estimators have been proposed, and their properties are studied under the assumptions that numbers of sampling units follow a distribution due to random non response situations. The performances of the proposed estimators are compared with linear combinations of two estimators, (a) sample mean estimator for fresh sample and (b) ratio estimator for matched sample under the complete response situations. Results are demonstrated through empirical studies which present the effectiveness of the proposed estimators. Suitable recommendations have been made to the survey practitioners. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=successive%20sampling" title="successive sampling">successive sampling</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20non-response" title=" random non-response"> random non-response</a>, <a href="https://publications.waset.org/abstracts/search?q=auxiliary%20variable" title=" auxiliary variable"> auxiliary variable</a>, <a href="https://publications.waset.org/abstracts/search?q=bias" title=" bias"> bias</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/78773/estimation-of-population-mean-under-random-non-response-in-two-phase-successive-sampling" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/78773.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">521</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">838</span> Estimation of Population Mean under Random Non-Response in Two-Occasion Successive Sampling </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Khalid">M. Khalid</a>, <a href="https://publications.waset.org/abstracts/search?q=G.%20N.%20Singh"> G. N. Singh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we have considered the problems of estimation for the population mean on current (second) occasion in two-occasion successive sampling under random non-response situations. Some modified exponential type estimators have been proposed and their properties are studied under the assumptions that the number of sampling unit follows a discrete distribution due to random non-response situations. The performances of the proposed estimators are compared with linear combinations of two estimators, (a) sample mean estimator for fresh sample and (b) ratio estimator for matched sample under the complete response situations. Results are demonstrated through empirical studies which present the effectiveness of the proposed estimators. Suitable recommendations have been made to the survey practitioners. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=modified%20exponential%20estimator" title="modified exponential estimator">modified exponential estimator</a>, <a href="https://publications.waset.org/abstracts/search?q=successive%20sampling" title=" successive sampling"> successive sampling</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20non-response" title=" random non-response"> random non-response</a>, <a href="https://publications.waset.org/abstracts/search?q=auxiliary%20variable" title=" auxiliary variable"> auxiliary variable</a>, <a href="https://publications.waset.org/abstracts/search?q=bias" title=" bias"> bias</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/85408/estimation-of-population-mean-under-random-non-response-in-two-occasion-successive-sampling" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/85408.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">349</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">837</span> The Normal-Generalized Hyperbolic Secant Distribution: Properties and Applications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hazem%20M.%20Al-Mofleh">Hazem M. Al-Mofleh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a new four-parameter univariate continuous distribution called the Normal-Generalized Hyperbolic Secant Distribution (NGHS) is defined and studied. Some general and structural distributional properties are investigated and discussed, including: central and non-central n-th moments and incomplete moments, quantile and generating functions, hazard function, Rényi and Shannon entropies, shapes: skewed right, skewed left, and symmetric, modality regions: unimodal and bimodal, maximum likelihood (MLE) estimators for the parameters. Finally, two real data sets are used to demonstrate empirically its flexibility and prove the strength of the new distribution. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bimodality" title="bimodality">bimodality</a>, <a href="https://publications.waset.org/abstracts/search?q=estimation" title=" estimation"> estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=hazard%20function" title=" hazard function"> hazard 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=Shannon%E2%80%99s%20entropy" title=" Shannon’s entropy"> Shannon’s entropy</a> </p> <a href="https://publications.waset.org/abstracts/62567/the-normal-generalized-hyperbolic-secant-distribution-properties-and-applications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/62567.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">348</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">836</span> On the Fractional Integration of Generalized Mittag-Leffler Type Functions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Christian%20Lavault">Christian Lavault</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, the generalized fractional integral operators of two generalized Mittag-Leffler type functions are investigated. The special cases of interest involve the generalized M-series and K-function, both introduced by Sharma. The two pairs of theorems established herein generalize recent results about left- and right-sided generalized fractional integration operators applied here to the M-series and the K-function. The note also results in important applications in physics and mathematical engineering. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fox%E2%80%93Wright%20Psi%20function" title="Fox–Wright Psi function">Fox–Wright Psi function</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20hypergeometric%20function" title=" generalized hypergeometric function"> generalized hypergeometric function</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20Riemann%E2%80%93%20Liouville%20and%20Erd%C3%A9lyi%E2%80%93Kober%20fractional%20integral%20operators" title=" generalized Riemann– Liouville and Erdélyi–Kober fractional integral operators"> generalized Riemann– Liouville and Erdélyi–Kober fractional integral operators</a>, <a href="https://publications.waset.org/abstracts/search?q=Saigo%27s%20generalized%20fractional%20calculus" title=" Saigo's generalized fractional calculus"> Saigo's generalized fractional calculus</a>, <a href="https://publications.waset.org/abstracts/search?q=Sharma%27s%20M-series%20and%20K-function" title=" Sharma's M-series and K-function"> Sharma's M-series and K-function</a> </p> <a href="https://publications.waset.org/abstracts/60662/on-the-fractional-integration-of-generalized-mittag-leffler-type-functions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/60662.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">440</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">835</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">834</span> Efficient Principal Components Estimation of Large Factor Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rachida%20Ouysse">Rachida Ouysse</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper proposes a constrained principal components (CnPC) estimator for efficient estimation of large-dimensional factor models when errors are cross sectionally correlated and the number of cross-sections (N) may be larger than the number of observations (T). Although principal components (PC) method is consistent for any path of the panel dimensions, it is inefficient as the errors are treated to be homoskedastic and uncorrelated. The new CnPC exploits the assumption of bounded cross-sectional dependence, which defines Chamberlain and Rothschild’s (1983) approximate factor structure, as an explicit constraint and solves a constrained PC problem. The CnPC method is computationally equivalent to the PC method applied to a regularized form of the data covariance matrix. Unlike maximum likelihood type methods, the CnPC method does not require inverting a large covariance matrix and thus is valid for panels with N ≥ T. The paper derives a convergence rate and an asymptotic normality result for the CnPC estimators of the common factors. We provide feasible estimators and show in a simulation study that they are more accurate than the PC estimator, especially for panels with N larger than T, and the generalized PC type estimators, especially for panels with N almost as large as T. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=high%20dimensionality" title="high dimensionality">high dimensionality</a>, <a href="https://publications.waset.org/abstracts/search?q=unknown%20factors" title=" unknown factors"> unknown factors</a>, <a href="https://publications.waset.org/abstracts/search?q=principal%20components" title=" principal components"> principal components</a>, <a href="https://publications.waset.org/abstracts/search?q=cross-sectional%20correlation" title=" cross-sectional correlation"> cross-sectional correlation</a>, <a href="https://publications.waset.org/abstracts/search?q=shrinkage%20regression" title=" shrinkage regression"> shrinkage regression</a>, <a href="https://publications.waset.org/abstracts/search?q=regularization" title=" regularization"> regularization</a>, <a href="https://publications.waset.org/abstracts/search?q=pseudo-out-of-sample%20forecasting" title=" pseudo-out-of-sample forecasting"> pseudo-out-of-sample forecasting</a> </p> <a href="https://publications.waset.org/abstracts/142686/efficient-principal-components-estimation-of-large-factor-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/142686.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">150</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">833</span> Ratio Type Estimators for the Estimation of Population Coefficient of Variation under Two-Stage Sampling</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Jabbar">Muhammad Jabbar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper we propose two ratio and ratio type exponential estimator for the estimation of population coefficient of variation using the auxiliary information under two-stage sampling. The properties of these estimators are derived up to first order of approximation. The efficiency conditions under which suggested estimator are more efficient, are obtained. Numerical and simulated studies are conducted to support the superiority of the estimators. Theoretically and numerically, we have found that our proposed estimator is always more efficient as compared to its competitor estimator. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=two-stage%20sampling" title="two-stage sampling">two-stage sampling</a>, <a href="https://publications.waset.org/abstracts/search?q=coefficient%20of%20variation" title=" coefficient of variation"> coefficient of variation</a>, <a href="https://publications.waset.org/abstracts/search?q=ratio%20type%20exponential%20estimator" title=" ratio type exponential estimator"> ratio type exponential estimator</a> </p> <a href="https://publications.waset.org/abstracts/21936/ratio-type-estimators-for-the-estimation-of-population-coefficient-of-variation-under-two-stage-sampling" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21936.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">528</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">832</span> Introduction of Robust Multivariate Process Capability Indices</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Behrooz%20Khalilloo">Behrooz Khalilloo</a>, <a href="https://publications.waset.org/abstracts/search?q=Hamid%20Shahriari"> Hamid Shahriari</a>, <a href="https://publications.waset.org/abstracts/search?q=Emad%20Roghanian"> Emad Roghanian</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Process capability indices (PCIs) are important concepts of statistical quality control and measure the capability of processes and how much processes are meeting certain specifications. An important issue in statistical quality control is parameter estimation. Under the assumption of multivariate normality, the distribution parameters, mean vector and variance-covariance matrix must be estimated, when they are unknown. Classic estimation methods like method of moment estimation (MME) or maximum likelihood estimation (MLE) makes good estimation of the population parameters when data are not contaminated. But when outliers exist in the data, MME and MLE make weak estimators of the population parameters. So we need some estimators which have good estimation in the presence of outliers. In this work robust M-estimators for estimating these parameters are used and based on robust parameter estimators, robust process capability indices are introduced. The performances of these robust estimators in the presence of outliers and their effects on process capability indices are evaluated by real and simulated multivariate data. The results indicate that the proposed robust capability indices perform much better than the existing process capability indices. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multivariate%20process%20capability%20indices" title="multivariate process capability indices">multivariate process capability indices</a>, <a href="https://publications.waset.org/abstracts/search?q=robust%20M-estimator" title=" robust M-estimator"> robust M-estimator</a>, <a href="https://publications.waset.org/abstracts/search?q=outlier" title=" outlier"> outlier</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20quality%20control" title=" multivariate quality control"> multivariate quality control</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20quality%20control" title=" statistical quality control"> statistical quality control</a> </p> <a href="https://publications.waset.org/abstracts/81586/introduction-of-robust-multivariate-process-capability-indices" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/81586.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">283</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">831</span> Estimation of the Mean of the Selected Population</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kalu%20Ram%20Meena">Kalu Ram Meena</a>, <a href="https://publications.waset.org/abstracts/search?q=Aditi%20Kar%20Gangopadhyay"> Aditi Kar Gangopadhyay</a>, <a href="https://publications.waset.org/abstracts/search?q=Satrajit%20Mandal"> Satrajit Mandal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Two normal populations with different means and same variance are considered, where the variances are known. The population with the smaller sample mean is selected. Various estimators are constructed for the mean of the selected normal population. Finally, they are compared with respect to the bias and MSE risks by the method of Monte-Carlo simulation and their performances are analysed with the help of graphs. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=estimation%20after%20selection" title="estimation after selection">estimation after selection</a>, <a href="https://publications.waset.org/abstracts/search?q=Brewster-Zidek%20technique" title=" Brewster-Zidek technique"> Brewster-Zidek technique</a>, <a href="https://publications.waset.org/abstracts/search?q=estimators" title=" estimators"> estimators</a>, <a href="https://publications.waset.org/abstracts/search?q=selected%20populations" title=" selected populations"> selected populations</a> </p> <a href="https://publications.waset.org/abstracts/17179/estimation-of-the-mean-of-the-selected-population" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17179.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">512</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">830</span> Bayes Estimation of Parameters of Binomial Type Rayleigh Class Software Reliability Growth Model using Non-informative Priors</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rajesh%20Singh">Rajesh Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=Kailash%20Kale"> Kailash Kale </a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, the Binomial process type occurrence of software failures is considered and failure intensity has been characterized by one parameter Rayleigh class Software Reliability Growth Model (SRGM). The proposed SRGM is mathematical function of parameters namely; total number of failures i.e. η-0 and scale parameter i.e. η-1. It is assumed that very little or no information is available about both these parameters and then considering non-informative priors for both these parameters, the Bayes estimators for the parameters η-0 and η-1 have been obtained under square error loss function. The proposed Bayes estimators are compared with their corresponding maximum likelihood estimators on the basis of risk efficiencies obtained by Monte Carlo simulation technique. It is concluded that both the proposed Bayes estimators of total number of failures and scale parameter perform well for proper choice of execution time. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=binomial%20process" title="binomial process">binomial process</a>, <a href="https://publications.waset.org/abstracts/search?q=non-informative%20prior" title=" non-informative prior"> non-informative prior</a>, <a href="https://publications.waset.org/abstracts/search?q=maximum%20likelihood%20estimator%20%28MLE%29" title=" maximum likelihood estimator (MLE)"> maximum likelihood estimator (MLE)</a>, <a href="https://publications.waset.org/abstracts/search?q=rayleigh%20class" title=" rayleigh class"> rayleigh class</a>, <a href="https://publications.waset.org/abstracts/search?q=software%20reliability%20growth%20model%20%28SRGM%29" title=" software reliability growth model (SRGM)"> software reliability growth model (SRGM)</a> </p> <a href="https://publications.waset.org/abstracts/8925/bayes-estimation-of-parameters-of-binomial-type-rayleigh-class-software-reliability-growth-model-using-non-informative-priors" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/8925.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">388</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">829</span> The Impact of Socialization Preferences on Perceptions of Generalized Social Trust in China</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Menghzheng%20Yao">Menghzheng Yao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Generalized social trust among Chinese has been declining in the past few decades, making the search for its causes necessary. Drawing on the symbolic interaction theory and the 2012 Chinese General Social Survey data, this research investigated the impact of people’s socialization preferences and frequencies on their perceptions of generalized social trust in China. This research also took a preliminary step towards understanding the spatial differences of the generalized social trust using the ArcGIS software. The results show that respondents who interacted with their neighbors more frequently were more likely to have higher levels of perceptions of generalized social trust. Several demographics were also significantly related to perception of generalized social trust. Elderly and better educated Chinese and people with higher self-perceived social status were associated with greater levels of generalized social trust perception, while urban dwellers and religious respondents expressed lower levels of such perception. Implications for future research and policy are discussed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=China" title="China">China</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20social%20trust" title=" generalized social trust"> generalized social trust</a>, <a href="https://publications.waset.org/abstracts/search?q=symbolic%20interaction" title=" symbolic interaction"> symbolic interaction</a>, <a href="https://publications.waset.org/abstracts/search?q=ArcGIS" title=" ArcGIS"> ArcGIS</a> </p> <a href="https://publications.waset.org/abstracts/70818/the-impact-of-socialization-preferences-on-perceptions-of-generalized-social-trust-in-china" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/70818.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">828</span> Nano Generalized Topology</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Y.%20Bakeir">M. Y. Bakeir</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Rough set theory is a recent approach for reasoning about data. It has achieved a large amount of applications in various real-life fields. The main idea of rough sets corresponds to the lower and upper set approximations. These two approximations are exactly the interior and the closure of the set with respect to a certain topology on a collection U of imprecise data acquired from any real-life field. The base of the topology is formed by equivalence classes of an equivalence relation E defined on U using the available information about data. The theory of generalized topology was studied by Cs´asz´ar. It is well known that generalized topology in the sense of Cs´asz´ar is a generalization of the topology on a set. On the other hand, many important collections of sets related with the topology on a set form a generalized topology. The notion of Nano topology was introduced by Lellis Thivagar, which was defined in terms of approximations and boundary region of a subset of an universe using an equivalence relation on it. The purpose of this paper is to introduce a new generalized topology in terms of rough set called nano generalized topology <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=rough%20sets" title="rough sets">rough sets</a>, <a href="https://publications.waset.org/abstracts/search?q=topological%20space" title=" topological space"> topological space</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20topology" title=" generalized topology"> generalized topology</a>, <a href="https://publications.waset.org/abstracts/search?q=nano%20topology" title=" nano topology "> nano topology </a> </p> <a href="https://publications.waset.org/abstracts/28088/nano-generalized-topology" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/28088.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">429</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">827</span> Bayesian Approach for Moving Extremes Ranked Set Sampling</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Said%20Ali%20Al-Hadhrami">Said Ali Al-Hadhrami</a>, <a href="https://publications.waset.org/abstracts/search?q=Amer%20Ibrahim%20Al-Omari"> Amer Ibrahim Al-Omari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, Bayesian estimation for the mean of exponential distribution is considered using Moving Extremes Ranked Set Sampling (MERSS). Three priors are used; Jeffery, conjugate and constant using MERSS and Simple Random Sampling (SRS). Some properties of the proposed estimators are investigated. It is found that the suggested estimators using MERSS are more efficient than its counterparts based on SRS. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bayesian" title="Bayesian">Bayesian</a>, <a href="https://publications.waset.org/abstracts/search?q=efficiency" title=" efficiency"> efficiency</a>, <a href="https://publications.waset.org/abstracts/search?q=moving%20extreme%20ranked%20set%20sampling" title=" moving extreme ranked set sampling"> moving extreme ranked set sampling</a>, <a href="https://publications.waset.org/abstracts/search?q=ranked%20set%20sampling" title=" ranked set sampling"> ranked set sampling</a> </p> <a href="https://publications.waset.org/abstracts/30733/bayesian-approach-for-moving-extremes-ranked-set-sampling" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/30733.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">513</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">826</span> A Comparative Study of Additive and Nonparametric Regression Estimators and Variable Selection Procedures</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Adriano%20Z.%20Zambom">Adriano Z. Zambom</a>, <a href="https://publications.waset.org/abstracts/search?q=Preethi%20Ravikumar"> Preethi Ravikumar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> One of the biggest challenges in nonparametric regression is the curse of dimensionality. Additive models are known to overcome this problem by estimating only the individual additive effects of each covariate. However, if the model is misspecified, the accuracy of the estimator compared to the fully nonparametric one is unknown. In this work the efficiency of completely nonparametric regression estimators such as the Loess is compared to the estimators that assume additivity in several situations, including additive and non-additive regression scenarios. The comparison is done by computing the oracle mean square error of the estimators with regards to the true nonparametric regression function. Then, a backward elimination selection procedure based on the Akaike Information Criteria is proposed, which is computed from either the additive or the nonparametric model. Simulations show that if the additive model is misspecified, the percentage of time it fails to select important variables can be higher than that of the fully nonparametric approach. A dimension reduction step is included when nonparametric estimator cannot be computed due to the curse of dimensionality. Finally, the Boston housing dataset is analyzed using the proposed backward elimination procedure and the selected variables are identified. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=additive%20model" title="additive model">additive model</a>, <a href="https://publications.waset.org/abstracts/search?q=nonparametric%20regression" title=" nonparametric regression"> nonparametric regression</a>, <a href="https://publications.waset.org/abstracts/search?q=variable%20selection" title=" variable selection"> variable selection</a>, <a href="https://publications.waset.org/abstracts/search?q=Akaike%20Information%20Criteria" title=" Akaike Information Criteria"> Akaike Information Criteria</a> </p> <a href="https://publications.waset.org/abstracts/56158/a-comparative-study-of-additive-and-nonparametric-regression-estimators-and-variable-selection-procedures" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/56158.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">264</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">825</span> Derivatives Formulas Involving I-Functions of Two Variables and Generalized M-Series</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gebreegziabher%20Hailu%20Gebrecherkos">Gebreegziabher Hailu Gebrecherkos</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study explores the derivatives of functions defined by I-functions of two variables and their connections to generalized M-series. We begin by defining I-functions, which are generalized functions that encompass various special functions, and analyze their properties. By employing advanced calculus techniques, we derive new formulas for the first and higher-order derivatives of I-functions with respect to their variables; we establish some derivative formulae of the I-function of two variables involving generalized M-series. The special cases of our derivatives yield interesting results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=I-function" title="I-function">I-function</a>, <a href="https://publications.waset.org/abstracts/search?q=Mellin-Barners%20control%20integral" title=" Mellin-Barners control integral"> Mellin-Barners control integral</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20M-series" title=" generalized M-series"> generalized M-series</a>, <a href="https://publications.waset.org/abstracts/search?q=higher%20order%20derivative" title=" higher order derivative"> higher order derivative</a> </p> <a href="https://publications.waset.org/abstracts/192292/derivatives-formulas-involving-i-functions-of-two-variables-and-generalized-m-series" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/192292.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">15</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">824</span> Generalized Central Paths for Convex Programming</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Li-Zhi%20Liao">Li-Zhi Liao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The central path has played the key role in the interior point method. However, the convergence of the central path may not be true even in some convex programming problems with linear constraints. In this paper, the generalized central paths are introduced for convex programming. One advantage of the generalized central paths is that the paths will always converge to some optimal solutions of the convex programming problem for any initial interior point. Some additional theoretical properties for the generalized central paths will be also reported. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=central%20path" title="central path">central path</a>, <a href="https://publications.waset.org/abstracts/search?q=convex%20programming" title=" convex programming"> convex programming</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20central%20path" title=" generalized central path"> generalized central path</a>, <a href="https://publications.waset.org/abstracts/search?q=interior%20point%20method" title=" interior point method"> interior point method</a> </p> <a href="https://publications.waset.org/abstracts/58039/generalized-central-paths-for-convex-programming" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/58039.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">327</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">823</span> Comparing Xbar Charts: Conventional versus Reweighted Robust Estimation Methods for Univariate Data Sets</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ece%20Cigdem%20Mutlu">Ece Cigdem Mutlu</a>, <a href="https://publications.waset.org/abstracts/search?q=Burak%20Alakent"> Burak Alakent</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Maintaining the quality of manufactured products at a desired level depends on the stability of process dispersion and location parameters and detection of perturbations in these parameters as promptly as possible. Shewhart control chart is the most widely used technique in statistical process monitoring to monitor the quality of products and control process mean and variability. In the application of Xbar control charts, sample standard deviation and sample mean are known to be the most efficient conventional estimators in determining process dispersion and location parameters, respectively, based on the assumption of independent and normally distributed datasets. On the other hand, there is no guarantee that the real-world data would be normally distributed. In the cases of estimated process parameters from Phase I data clouded with outliers, efficiency of traditional estimators is significantly reduced, and performance of Xbar charts are undesirably low, e.g. occasional outliers in the rational subgroups in Phase I data set may considerably affect the sample mean and standard deviation, resulting a serious delay in detection of inferior products in Phase II. For more efficient application of control charts, it is required to use robust estimators against contaminations, which may exist in Phase I. In the current study, we present a simple approach to construct robust Xbar control charts using average distance to the median, Qn-estimator of scale, M-estimator of scale with logistic psi-function in the estimation of process dispersion parameter, and Harrell-Davis qth quantile estimator, Hodge-Lehmann estimator and M-estimator of location with Huber psi-function and logistic psi-function in the estimation of process location parameter. Phase I efficiency of proposed estimators and Phase II performance of Xbar charts constructed from these estimators are compared with the conventional mean and standard deviation statistics both under normality and against diffuse-localized and symmetric-asymmetric contaminations using 50,000 Monte Carlo simulations on MATLAB. Consequently, it is found that robust estimators yield parameter estimates with higher efficiency against all types of contaminations, and Xbar charts constructed using robust estimators have higher power in detecting disturbances, compared to conventional methods. Additionally, utilizing individuals charts to screen outlier subgroups and employing different combination of dispersion and location estimators on subgroups and individual observations are found to improve the performance of Xbar charts. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=average%20run%20length" title="average run length">average run length</a>, <a href="https://publications.waset.org/abstracts/search?q=M-estimators" title=" M-estimators"> M-estimators</a>, <a href="https://publications.waset.org/abstracts/search?q=quality%20control" title=" quality control"> quality control</a>, <a href="https://publications.waset.org/abstracts/search?q=robust%20estimators" title=" robust estimators"> robust estimators</a> </p> <a href="https://publications.waset.org/abstracts/79020/comparing-xbar-charts-conventional-versus-reweighted-robust-estimation-methods-for-univariate-data-sets" class="btn btn-primary btn-sm">Procedia</a> 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