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distributions</title> <meta name="description" content="Search results for: distributions"> <meta name="keywords" content="distributions"> <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 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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="distributions"> <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> 656</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: distributions</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">656</span> A Proposed Mechanism for Skewing Symmetric Distributions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20T.%20Alodat">M. T. Alodat</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we propose a mechanism for skewing any symmetric distribution. The new distribution is called the deflation-inflation distribution (DID). We discuss some statistical properties of the DID such moments, stochastic representation, log-concavity. Also we fit the distribution to real data and we compare it to normal distribution and Azzlaini's skew normal distribution. Numerical results show that the DID fits the the tree ring data better than the other two distributions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=normal%20distribution" title="normal distribution">normal distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=moments" title=" moments"> moments</a>, <a href="https://publications.waset.org/abstracts/search?q=Fisher%20information" title=" Fisher information"> Fisher information</a>, <a href="https://publications.waset.org/abstracts/search?q=symmetric%20distributions" title=" symmetric distributions"> symmetric distributions</a> </p> <a href="https://publications.waset.org/abstracts/28593/a-proposed-mechanism-for-skewing-symmetric-distributions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/28593.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">659</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">655</span> Beyond Classic Program Evaluation and Review Technique: A Generalized Model for Subjective Distributions with Flexible Variance</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Byung%20Cheol%20Kim">Byung Cheol Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Program Evaluation and Review Technique (PERT) is widely used for project management, but it struggles with subjective distributions, particularly due to its assumptions of constant variance and light tails. To overcome these limitations, we propose the Generalized PERT (G-PERT) model, which enhances PERT by incorporating variability in three-point subjective estimates. Our methodology extends the original PERT model to cover the full range of unimodal beta distributions, enabling the model to handle thick-tailed distributions and offering formulas for computing mean and variance. This maintains the simplicity of PERT while providing a more accurate depiction of uncertainty. Our empirical analysis demonstrates that the G-PERT model significantly improves performance, particularly when dealing with heavy-tail subjective distributions. In comparative assessments with alternative models such as triangular and lognormal distributions, G-PERT shows superior accuracy and flexibility. These results suggest that G-PERT offers a more robust solution for project estimation while still retaining the user-friendliness of the classic PERT approach. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=PERT" title="PERT">PERT</a>, <a href="https://publications.waset.org/abstracts/search?q=subjective%20distribution" title=" subjective distribution"> subjective distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=project%20management" title=" project management"> project management</a>, <a href="https://publications.waset.org/abstracts/search?q=flexible%20variance" title=" flexible variance"> flexible variance</a> </p> <a href="https://publications.waset.org/abstracts/192135/beyond-classic-program-evaluation-and-review-technique-a-generalized-model-for-subjective-distributions-with-flexible-variance" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/192135.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">18</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">654</span> Copula Markov Switching Multifractal Models for Forecasting Value-at-Risk </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Giriraj%20Achari">Giriraj Achari</a>, <a href="https://publications.waset.org/abstracts/search?q=Malay%20Bhattacharyya"> Malay Bhattacharyya</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, the effectiveness of Copula Markov Switching Multifractal (MSM) models at forecasting Value-at-Risk of a two-stock portfolio is studied. The innovations are allowed to be drawn from distributions that can capture skewness and leptokurtosis, which are well documented empirical characteristics observed in financial returns. The candidate distributions considered for this purpose are Johnson-SU, Pearson Type-IV and α-Stable distributions. The two univariate marginal distributions are combined using the Student-t copula. The estimation of all parameters is performed by Maximum Likelihood Estimation. Finally, the models are compared in terms of accurate Value-at-Risk (VaR) forecasts using tests of unconditional coverage and independence. It is found that Copula-MSM-models with leptokurtic innovation distributions perform slightly better than Copula-MSM model with Normal innovations. Copula-MSM models, in general, produce better VaR forecasts as compared to traditional methods like Historical Simulation method, Variance-Covariance approach and Copula-Generalized Autoregressive Conditional Heteroscedasticity (Copula-GARCH) models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Copula" title="Copula">Copula</a>, <a href="https://publications.waset.org/abstracts/search?q=Markov%20Switching" title=" Markov Switching"> Markov Switching</a>, <a href="https://publications.waset.org/abstracts/search?q=multifractal" title=" multifractal"> multifractal</a>, <a href="https://publications.waset.org/abstracts/search?q=value-at-risk" title=" value-at-risk"> value-at-risk</a> </p> <a href="https://publications.waset.org/abstracts/115727/copula-markov-switching-multifractal-models-for-forecasting-value-at-risk" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/115727.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">165</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">653</span> X̄ and S Control Charts based on Weighted Standard Deviation Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Derya%20Karag%C3%B6z">Derya Karagöz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A Shewhart chart based on normality assumption is not appropriate for skewed distributions since its Type-I error rate is inflated. This study presents X̄ and S control charts for monitoring the process variability for skewed distributions. We propose Weighted Standard Deviation (WSD) X̄ and S control charts. Standard deviation estimator is applied to monitor the process variability for estimating the process standard deviation, in the case of the W SD X̄ and S control charts as this estimator is simple and easy to compute. Unlike the Shewhart control chart, the proposed charts provide asymmetric limits in accordance with the direction and degree of skewness to construct the upper and lower limits. The performances of the proposed charts are compared with other heuristic charts for skewed distributions by using Simulation study. The Simulation studies show that the proposed control charts have good properties for skewed distributions and large sample sizes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=weighted%20standard%20deviation" title="weighted standard deviation">weighted standard deviation</a>, <a href="https://publications.waset.org/abstracts/search?q=MAD" title=" MAD"> MAD</a>, <a href="https://publications.waset.org/abstracts/search?q=skewed%20distributions" title=" skewed distributions"> skewed distributions</a>, <a href="https://publications.waset.org/abstracts/search?q=S%20control%20charts" title=" S control charts"> S control charts</a> </p> <a href="https://publications.waset.org/abstracts/45730/x-and-s-control-charts-based-on-weighted-standard-deviation-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45730.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">399</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">652</span> Characterization of Probability Distributions through Conditional Expectation of Pair of Generalized Order Statistics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zubdahe%20Noor">Zubdahe Noor</a>, <a href="https://publications.waset.org/abstracts/search?q=Haseeb%20Athar"> Haseeb Athar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this article, first a relation for conditional expectation is developed and then is used to characterize a general class of distributions F(x) = 1-e^(-ah(x)) through conditional expectation of difference of pair of generalized order statistics. Some results are reduced for particular cases. In the end, a list of distributions is presented in the form of table that are compatible with the given general class. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=generalized%20order%20statistics" title="generalized order statistics">generalized order statistics</a>, <a href="https://publications.waset.org/abstracts/search?q=order%20statistics" title=" order statistics"> order statistics</a>, <a href="https://publications.waset.org/abstracts/search?q=record%20values" title=" record values"> record values</a>, <a href="https://publications.waset.org/abstracts/search?q=conditional%20expectation" title=" conditional expectation"> conditional expectation</a>, <a href="https://publications.waset.org/abstracts/search?q=characterization" title=" characterization"> characterization</a> </p> <a href="https://publications.waset.org/abstracts/22898/characterization-of-probability-distributions-through-conditional-expectation-of-pair-of-generalized-order-statistics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/22898.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">460</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">651</span> Classification on Statistical Distributions of a Complex N-Body System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=David%20C.%20Ni">David C. Ni</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Contemporary models for N-body systems are based on temporal, two-body, and mass point representation of Newtonian mechanics. Other mainstream models include 2D and 3D Ising models based on local neighborhood the lattice structures. In Quantum mechanics, the theories of collective modes are for superconductivity and for the long-range quantum entanglement. However, these models are still mainly for the specific phenomena with a set of designated parameters. We are therefore motivated to develop a new construction directly from the complex-variable N-body systems based on the extended Blaschke functions (EBF), which represent a non-temporal and nonlinear extension of Lorentz transformation on the complex plane – the normalized momentum spaces. A point on the complex plane represents a normalized state of particle momentums observed from a reference frame in the theory of special relativity. There are only two key parameters, normalized momentum and nonlinearity for modelling. An algorithm similar to Jenkins-Traub method is adopted for solving EBF iteratively. Through iteration, the solution sets show a form of σ + i [-t, t], where σ and t are the real numbers, and the [-t, t] shows various distributions, such as 1-peak, 2-peak, and 3-peak etc. distributions and some of them are analog to the canonical distributions. The results of the numerical analysis demonstrate continuum-to-discreteness transitions, evolutional invariance of distributions, phase transitions with conjugate symmetry, etc., which manifest the construction as a potential candidate for the unification of statistics. We hereby classify the observed distributions on the finite convergent domains. Continuous and discrete distributions both exist and are predictable for given partitions in different regions of parameter-pair. We further compare these distributions with canonical distributions and address the impacts on the existing applications. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=blaschke" title="blaschke">blaschke</a>, <a href="https://publications.waset.org/abstracts/search?q=lorentz%20transformation" title=" lorentz transformation"> lorentz transformation</a>, <a href="https://publications.waset.org/abstracts/search?q=complex%20variables" title=" complex variables"> complex variables</a>, <a href="https://publications.waset.org/abstracts/search?q=continuous" title=" continuous"> continuous</a>, <a href="https://publications.waset.org/abstracts/search?q=discrete" title=" discrete"> discrete</a>, <a href="https://publications.waset.org/abstracts/search?q=canonical" title=" canonical"> canonical</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a> </p> <a href="https://publications.waset.org/abstracts/51005/classification-on-statistical-distributions-of-a-complex-n-body-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51005.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">309</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">650</span> Forecasting for Financial Stock Returns Using a Quantile Function Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yuzhi%20Cai">Yuzhi Cai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we introduce a newly developed quantile function model that can be used for estimating conditional distributions of financial returns and for obtaining multi-step ahead out-of-sample predictive distributions of financial returns. Since we forecast the whole conditional distributions, any predictive quantity of interest about the future financial returns can be obtained simply as a by-product of the method. We also show an application of the model to the daily closing prices of Dow Jones Industrial Average (DJIA) series over the period from 2 January 2004 - 8 October 2010. We obtained the predictive distributions up to 15 days ahead for the DJIA returns, which were further compared with the actually observed returns and those predicted from an AR-GARCH model. The results show that the new model can capture the main features of financial returns and provide a better fitted model together with improved mean forecasts compared with conventional methods. We hope this talk will help audience to see that this new model has the potential to be very useful in practice. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=DJIA" title="DJIA">DJIA</a>, <a href="https://publications.waset.org/abstracts/search?q=financial%20returns" title=" financial returns"> financial returns</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20distribution" title=" predictive distribution"> predictive distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=quantile%20function%20model" title=" quantile function model"> quantile function model</a> </p> <a href="https://publications.waset.org/abstracts/33434/forecasting-for-financial-stock-returns-using-a-quantile-function-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33434.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">367</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">649</span> First Order Moment Bounds on DMRL and IMRL Classes of Life Distributions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Debasis%20Sengupta">Debasis Sengupta</a>, <a href="https://publications.waset.org/abstracts/search?q=Sudipta%20Das"> Sudipta Das</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The class of life distributions with decreasing mean residual life (DMRL) is well known in the field of reliability modeling. It contains the IFR class of distributions and is contained in the NBUE class of distributions. While upper and lower bounds of the reliability distribution function of aging classes such as IFR, IFRA, NBU, NBUE, and HNBUE have discussed in the literature for a long time, there is no analogous result available for the DMRL class. We obtain the upper and lower bounds for the reliability function of the DMRL class in terms of first order finite moment. The lower bound is obtained by showing that for any fixed time, the minimization of the reliability function over the class of all DMRL distributions with a fixed mean is equivalent to its minimization over a smaller class of distribution with a special form. Optimization over this restricted set can be made algebraically. Likewise, the maximization of the reliability function over the class of all DMRL distributions with a fixed mean turns out to be a parametric optimization problem over the class of DMRL distributions of a special form. The constructive proofs also establish that both the upper and lower bounds are sharp. Further, the DMRL upper bound coincides with the HNBUE upper bound and the lower bound coincides with the IFR lower bound. We also prove that a pair of sharp upper and lower bounds for the reliability function when the distribution is increasing mean residual life (IMRL) with a fixed mean. This result is proved in a similar way. These inequalities fill a long-standing void in the literature of the life distribution modeling. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=DMRL" title="DMRL">DMRL</a>, <a href="https://publications.waset.org/abstracts/search?q=IMRL" title=" IMRL"> IMRL</a>, <a href="https://publications.waset.org/abstracts/search?q=reliability%20bounds" title=" reliability bounds"> reliability bounds</a>, <a href="https://publications.waset.org/abstracts/search?q=hazard%20functions" title=" hazard functions"> hazard functions</a> </p> <a href="https://publications.waset.org/abstracts/47988/first-order-moment-bounds-on-dmrl-and-imrl-classes-of-life-distributions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/47988.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">648</span> Determining Best Fitting Distributions for Minimum Flows of Streams in Gediz Basin</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Naci%20B%C3%BCy%C3%BCkkarac%C4%B1%C4%9Fan">Naci Büyükkaracığan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Today, the need for water sources is swiftly increasing due to population growth. At the same time, it is known that some regions will face with shortage of water and drought because of the global warming and climate change. In this context, evaluation and analysis of hydrological data such as the observed trends, drought and flood prediction of short term flow has great deal of importance. The most accurate selection probability distribution is important to describe the low flow statistics for the studies related to drought analysis. As in many basins In Turkey, Gediz River basin will be affected enough by the drought and will decrease the amount of used water. The aim of this study is to derive appropriate probability distributions for frequency analysis of annual minimum flows at 6 gauging stations of the Gediz Basin. After applying 10 different probability distributions, six different parameter estimation methods and 3 fitness test, the Pearson 3 distribution and general extreme values distributions were found to give optimal results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gediz%20Basin" title="Gediz Basin">Gediz Basin</a>, <a href="https://publications.waset.org/abstracts/search?q=goodness-of-fit%20tests" title=" goodness-of-fit tests"> goodness-of-fit tests</a>, <a href="https://publications.waset.org/abstracts/search?q=minimum%20flows" title=" minimum flows"> minimum flows</a>, <a href="https://publications.waset.org/abstracts/search?q=probability%20distribution" title=" probability distribution"> probability distribution</a> </p> <a href="https://publications.waset.org/abstracts/9078/determining-best-fitting-distributions-for-minimum-flows-of-streams-in-gediz-basin" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9078.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">271</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">647</span> Frequency Analysis Using Multiple Parameter Probability Distributions for Rainfall to Determine Suitable Probability Distribution in Pakistan</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tasir%20Khan">Tasir Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=Yejuan%20Wang"> Yejuan Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The study of extreme rainfall events is very important for flood management in river basins and the design of water conservancy infrastructure. Evaluation of quantiles of annual maximum rainfall (AMRF) is required in different environmental fields, agriculture operations, renewable energy sources, climatology, and the design of different structures. Therefore, the annual maximum rainfall (AMRF) was performed at different stations in Pakistan. Multiple probability distributions, log normal (LN), generalized extreme value (GEV), Gumbel (max), and Pearson type3 (P3) were used to find out the most appropriate distributions in different stations. The L moments method was used to evaluate the distribution parameters. Anderson darling test, Kolmogorov- Smirnov test, and chi-square test showed that two distributions, namely GUM (max) and LN, were the best appropriate distributions. The quantile estimate of a multi-parameter PD offers extreme rainfall through a specific location and is therefore important for decision-makers and planners who design and construct different structures. This result provides an indication of these multi-parameter distribution consequences for the study of sites and peak flow prediction and the design of hydrological maps. Therefore, this discovery can support hydraulic structure and flood management. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=RAMSE" title="RAMSE">RAMSE</a>, <a href="https://publications.waset.org/abstracts/search?q=multiple%20frequency%20analysis" title=" multiple frequency analysis"> multiple frequency analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=annual%20maximum%20rainfall" title=" annual maximum rainfall"> annual maximum rainfall</a>, <a href="https://publications.waset.org/abstracts/search?q=L-moments" title=" L-moments"> L-moments</a> </p> <a href="https://publications.waset.org/abstracts/161973/frequency-analysis-using-multiple-parameter-probability-distributions-for-rainfall-to-determine-suitable-probability-distribution-in-pakistan" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/161973.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">81</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">646</span> Schrödinger Equation with Position-Dependent Mass: Staggered Mass Distributions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=J.%20J.%20Pe%C3%B1a">J. J. Peña</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20Morales"> J. Morales</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20Garc%C3%ADa-Ravelo"> J. García-Ravelo</a>, <a href="https://publications.waset.org/abstracts/search?q=L.%20Arcos-D%C3%ADaz"> L. Arcos-Díaz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Point canonical transformation method is applied for solving the Schrödinger equation with position-dependent mass. This class of problem has been solved for continuous mass distributions. In this work, a staggered mass distribution for the case of a free particle in an infinite square well potential has been proposed. The continuity conditions as well as normalization for the wave function are also considered. The proposal can be used for dealing with other kind of staggered mass distributions in the Schrödinger equation with different quantum potentials. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=free%20particle" title="free particle">free particle</a>, <a href="https://publications.waset.org/abstracts/search?q=point%20canonical%20transformation%20method" title=" point canonical transformation method"> point canonical transformation method</a>, <a href="https://publications.waset.org/abstracts/search?q=position-dependent%20mass" title=" position-dependent mass"> position-dependent mass</a>, <a href="https://publications.waset.org/abstracts/search?q=staggered%20mass%20distribution" title=" staggered mass distribution"> staggered mass distribution</a> </p> <a href="https://publications.waset.org/abstracts/71082/schrodinger-equation-with-position-dependent-mass-staggered-mass-distributions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/71082.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">403</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">645</span> An Application of Modified M-out-of-N Bootstrap Method to Heavy-Tailed Distributions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hannah%20F.%20Opayinka">Hannah F. Opayinka</a>, <a href="https://publications.waset.org/abstracts/search?q=Adedayo%20A.%20Adepoju"> Adedayo A. Adepoju </a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study is an extension of a prior study on the modification of the existing m-out-of-n (moon) bootstrap method for heavy-tailed distributions in which modified m-out-of-n (mmoon) was proposed as an alternative method to the existing moon technique. In this study, both moon and mmoon techniques were applied to two real income datasets which followed Lognormal and Pareto distributions respectively with finite variances. The performances of these two techniques were compared using Standard Error (SE) and Root Mean Square Error (RMSE). The findings showed that mmoon outperformed moon bootstrap in terms of smaller SEs and RMSEs for all the sample sizes considered in the two datasets. <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=income%20data" title=" income data"> income data</a>, <a href="https://publications.waset.org/abstracts/search?q=lognormal%20distribution" title=" lognormal distribution"> lognormal distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=Pareto%20distribution" title=" Pareto distribution"> Pareto distribution</a> </p> <a href="https://publications.waset.org/abstracts/104327/an-application-of-modified-m-out-of-n-bootstrap-method-to-heavy-tailed-distributions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/104327.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">186</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">644</span> Evaluating Forecasts Through Stochastic Loss Order</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wilmer%20Osvaldo%20Martinez">Wilmer Osvaldo Martinez</a>, <a href="https://publications.waset.org/abstracts/search?q=Manuel%20Dario%20Hernandez"> Manuel Dario Hernandez</a>, <a href="https://publications.waset.org/abstracts/search?q=Juan%20Manuel%20Julio"> Juan Manuel Julio</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We propose to assess the performance of k forecast procedures by exploring the distributions of forecast errors and error losses. We argue that non systematic forecast errors minimize when their distributions are symmetric and unimodal, and that forecast accuracy should be assessed through stochastic loss order rather than expected loss order, which is the way it is customarily performed in previous work. Moreover, since forecast performance evaluation can be understood as a one way analysis of variance, we propose to explore loss distributions under two circumstances; when a strict (but unknown) joint stochastic order exists among the losses of all forecast alternatives, and when such order happens among subsets of alternative procedures. In spite of the fact that loss stochastic order is stronger than loss moment order, our proposals are at least as powerful as competing tests, and are robust to the correlation, autocorrelation and heteroskedasticity settings they consider. In addition, since our proposals do not require samples of the same size, their scope is also wider, and provided that they test the whole loss distribution instead of just loss moments, they can also be used to study forecast distributions as well. We illustrate the usefulness of our proposals by evaluating a set of real world forecasts. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=forecast%20evaluation" title="forecast evaluation">forecast evaluation</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20order" title=" stochastic order"> stochastic order</a>, <a href="https://publications.waset.org/abstracts/search?q=multiple%20comparison" title=" multiple comparison"> multiple comparison</a>, <a href="https://publications.waset.org/abstracts/search?q=non%20parametric%20test" title=" non parametric test"> non parametric test</a> </p> <a href="https://publications.waset.org/abstracts/167870/evaluating-forecasts-through-stochastic-loss-order" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/167870.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">89</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">643</span> Hybrid EMPCA-Scott Approach for Estimating Probability Distributions of Mutual Information</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Thuvanan%20Borvornvitchotikarn">Thuvanan Borvornvitchotikarn</a>, <a href="https://publications.waset.org/abstracts/search?q=Werasak%20Kurutach"> Werasak Kurutach</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Mutual information (MI) is widely used in medical image registration. In the different medical images analysis, it is difficult to choose an optimal bins size number for calculating the probability distributions in MI. As the result, this paper presents a new adaptive bins number selection approach that named a hybrid EMPCA-Scott approach. This work combines an expectation maximization principal component analysis (EMPCA) and the modified Scott’s rule. The proposed approach solves the binning problem from the various intensity values in medical images. Experimental results of this work show the lower registration errors compared to other adaptive binning approaches. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mutual%20information" title="mutual information">mutual information</a>, <a href="https://publications.waset.org/abstracts/search?q=EMPCA" title=" EMPCA"> EMPCA</a>, <a href="https://publications.waset.org/abstracts/search?q=Scott" title=" Scott"> Scott</a>, <a href="https://publications.waset.org/abstracts/search?q=probability%20distributions" title=" probability distributions"> probability distributions</a> </p> <a href="https://publications.waset.org/abstracts/57641/hybrid-empca-scott-approach-for-estimating-probability-distributions-of-mutual-information" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/57641.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">249</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">642</span> A Strategy for the Application of Second-Order Monte Carlo Algorithms to Petroleum Exploration and Production Projects</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Obioma%20Uche">Obioma Uche</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Due to the recent volatility in oil & gas prices as well as increased development of non-conventional resources, it has become even more essential to critically evaluate the profitability of petroleum prospects prior to making any investment decisions. Traditionally, simple Monte Carlo (MC) algorithms have been used to randomly sample probability distributions of economic and geological factors (e.g. price, OPEX, CAPEX, reserves, productive life, etc.) in order to obtain probability distributions for profitability metrics such as Net Present Value (NPV). In recent years, second-order MC algorithms have been shown to offer an advantage over simple MC techniques due to the added consideration of uncertainties associated with the probability distributions of the relevant variables. Here, a strategy for the application of the second-order MC technique to a case study is demonstrated to analyze its effectiveness as a tool for portfolio management. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Monte%20Carlo%20algorithms" title="Monte Carlo algorithms">Monte Carlo algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=portfolio%20management" title=" portfolio management"> portfolio management</a>, <a href="https://publications.waset.org/abstracts/search?q=profitability" title=" profitability"> profitability</a>, <a href="https://publications.waset.org/abstracts/search?q=risk%20analysis" title=" risk analysis"> risk analysis</a> </p> <a href="https://publications.waset.org/abstracts/56718/a-strategy-for-the-application-of-second-order-monte-carlo-algorithms-to-petroleum-exploration-and-production-projects" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/56718.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">335</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">641</span> Marginalized Two-Part Joint Models for Generalized Gamma Family of Distributions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohadeseh%20Shojaei%20Shahrokhabadi">Mohadeseh Shojaei Shahrokhabadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Ding-Geng%20%28Din%29%20Chen"> Ding-Geng (Din) Chen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Positive continuous outcomes with a substantial number of zero values and incomplete longitudinal follow-up are quite common in medical cost data. To jointly model semi-continuous longitudinal cost data and survival data and to provide marginalized covariate effect estimates, a marginalized two-part joint model (MTJM) has been developed for outcome variables with lognormal distributions. In this paper, we propose MTJM models for outcome variables from a generalized gamma (GG) family of distributions. The GG distribution constitutes a general family that includes approximately all of the most frequently used distributions like the Gamma, Exponential, Weibull, and Log Normal. In the proposed MTJM-GG model, the conditional mean from a conventional two-part model with a three-parameter GG distribution is parameterized to provide the marginal interpretation for regression coefficients. In addition, MTJM-gamma and MTJM-Weibull are developed as special cases of MTJM-GG. To illustrate the applicability of the MTJM-GG, we applied the model to a set of real electronic health record data recently collected in Iran, and we provided SAS code for application. The simulation results showed that when the outcome distribution is unknown or misspecified, which is usually the case in real data sets, the MTJM-GG consistently outperforms other models. The GG family of distribution facilitates estimating a model with improved fit over the MTJM-gamma, standard Weibull, or Log-Normal distributions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=marginalized%20two-part%20model" title="marginalized two-part model">marginalized two-part model</a>, <a href="https://publications.waset.org/abstracts/search?q=zero-inflated" title=" zero-inflated"> zero-inflated</a>, <a href="https://publications.waset.org/abstracts/search?q=right-skewed" title=" right-skewed"> right-skewed</a>, <a href="https://publications.waset.org/abstracts/search?q=semi-continuous" title=" semi-continuous"> semi-continuous</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20gamma" title=" generalized gamma"> generalized gamma</a> </p> <a href="https://publications.waset.org/abstracts/140917/marginalized-two-part-joint-models-for-generalized-gamma-family-of-distributions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/140917.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">176</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">640</span> A Family of Distributions on Learnable Problems without Uniform Convergence</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=C%C3%A9sar%20Garza">César Garza</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In supervised binary classification and regression problems, it is well-known that learnability is equivalent to a uniform convergence of the hypothesis class, and if a problem is learnable, it is learnable by empirical risk minimization. For the general learning setting of unsupervised learning tasks, there are non-trivial learning problems where uniform convergence does not hold. We present here the task of learning centers of mass with an extra feature that “activates” some of the coordinates over the unit ball in a Hilbert space. We show that the learning problem is learnable under a stable RLM rule. We introduce a family of distributions over the domain space with some mild restrictions for which the sample complexity of uniform convergence for these problems must grow logarithmically with the dimension of the Hilbert space. If we take this dimension to infinity, we obtain a learnable problem for which the uniform convergence property fails for a vast family of distributions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=statistical%20learning%20theory" title="statistical learning theory">statistical learning theory</a>, <a href="https://publications.waset.org/abstracts/search?q=learnability" title=" learnability"> learnability</a>, <a href="https://publications.waset.org/abstracts/search?q=uniform%20convergence" title=" uniform convergence"> uniform convergence</a>, <a href="https://publications.waset.org/abstracts/search?q=stability" title=" stability"> stability</a>, <a href="https://publications.waset.org/abstracts/search?q=regularized%20loss%20minimization" title=" regularized loss minimization"> regularized loss minimization</a> </p> <a href="https://publications.waset.org/abstracts/151038/a-family-of-distributions-on-learnable-problems-without-uniform-convergence" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/151038.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">129</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">639</span> Parameters Estimation of Multidimensional Possibility Distributions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sergey%20Sorokin">Sergey Sorokin</a>, <a href="https://publications.waset.org/abstracts/search?q=Irina%20Sorokina"> Irina Sorokina</a>, <a href="https://publications.waset.org/abstracts/search?q=Alexander%20Yazenin"> Alexander Yazenin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We present a solution to the Maxmin u/E parameters estimation problem of possibility distributions in m-dimensional case. Our method is based on geometrical approach, where minimal area enclosing ellipsoid is constructed around the sample. Also we demonstrate that one can improve results of well-known algorithms in fuzzy model identification task using Maxmin u/E parameters estimation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=possibility%20distribution" title="possibility distribution">possibility distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=parameters%20estimation" title=" parameters estimation"> parameters estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=Maxmin%20u%5CE%20estimator" title=" Maxmin u\E estimator"> Maxmin u\E estimator</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20model%20identification" title=" fuzzy model identification"> fuzzy model identification</a> </p> <a href="https://publications.waset.org/abstracts/16751/parameters-estimation-of-multidimensional-possibility-distributions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16751.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">470</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">638</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">198</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">637</span> Tumour Radionuclides Therapy: in vitro and in vivo Dose Distribution Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rekaya%20A.%20Shabbir">Rekaya A. Shabbir</a>, <a href="https://publications.waset.org/abstracts/search?q=Marco%20Mingarelli"> Marco Mingarelli</a>, <a href="https://publications.waset.org/abstracts/search?q=Glenn%20Flux"> Glenn Flux</a>, <a href="https://publications.waset.org/abstracts/search?q=Ananya%20Choudhury"> Ananya Choudhury</a>, <a href="https://publications.waset.org/abstracts/search?q=Tim%20A.%20D.%20Smith"> Tim A. D. Smith</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Introduction: Heterogeneity of dose distributions across a tumour is problematic for targeted radiotherapy. Gold nanoparticles (AuNPs) enhance dose-distributions of targeted radionuclides. The aim of this study is to demonstrate if tumour dose-distribution of targeted AuNPs radiolabelled with either of two radioisotopes (¹⁷⁷Lu and ⁹⁰Y) in breast cancer cells produced homogeneous dose distributions. Moreover, in vitro and in vivo studies were conducted to study the importance of receptor level on cytotoxicity of EGFR-targeted AuNPs in breast and colorectal cancer cells. Methods: AuNPs were functionalised with DOTA and OPPS-PEG-SVA to optimise labelling with radionuclide tracers and targeting with Erbitux. Radionuclides were chelated with DOTA, and the uptake of the radiolabelled AuNPs and targeted activity in vitro in both cell lines measured using liquid scintillation counting. Cells with medium (HCT8) and high (MDA-MB-468) EGFR expression were incubated with targeted ¹⁷⁷Lu-AuNPs for 4h, then washed and allowed to form colonies. Nude mice bearing tumours were used to study the biodistribution by injecting ¹⁷⁷Lu-AuNPs or ⁹⁰Y-AuNPs via the tail vein. Heterogeneity of dose-distribution in tumours was determined using autoradiography. Results: Colony formation (% control) was 81 ± 4.7% (HCT8) and 32 ± 9% (MDA-MB-468). High uptake was observed in the liver and spleen, indicating hepatobiliary excretion. Imaging showed heterogeneity in dose-distributions for both radionuclides across the tumours. Conclusion: The cytotoxic effect of EGFR-targeted AuNPs is greater in cells with higher EGFR expression. Dose-distributions for individual radiolabelled nanoparticles were heterogeneous across tumours. Further strategies are required to improve the uniformity of dose distribution prior to clinical trials. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cancer%20cells" title="cancer cells">cancer cells</a>, <a href="https://publications.waset.org/abstracts/search?q=dose%20distributions" title=" dose distributions"> dose distributions</a>, <a href="https://publications.waset.org/abstracts/search?q=radionuclide%20therapy" title=" radionuclide therapy"> radionuclide therapy</a>, <a href="https://publications.waset.org/abstracts/search?q=targeted%20gold%20nanoparticles" title=" targeted gold nanoparticles"> targeted gold nanoparticles</a> </p> <a href="https://publications.waset.org/abstracts/134283/tumour-radionuclides-therapy-in-vitro-and-in-vivo-dose-distribution-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/134283.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">114</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">636</span> Effect of Variable Fluxes on Optimal Flux Distribution in a Metabolic Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ehsan%20Motamedian">Ehsan Motamedian</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Finding all optimal flux distributions of a metabolic model is an important challenge in systems biology. In this paper, a new algorithm is introduced to identify all alternate optimal solutions of a large scale metabolic network. The algorithm reduces the model to decrease computations for finding optimal solutions. The algorithm was implemented on the Escherichia coli metabolic model to find all optimal solutions for lactate and acetate production. There were more optimal flux distributions when acetate production was optimized. The model was reduced from 1076 to 80 variable fluxes for lactate while it was reduced to 91 variable fluxes for acetate. These 11 more variable fluxes resulted in about three times more optimal flux distributions. Variable fluxes were from 12 various metabolic pathways and most of them belonged to nucleotide salvage and extra cellular transport pathways. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=flux%20variability" title="flux variability">flux variability</a>, <a href="https://publications.waset.org/abstracts/search?q=metabolic%20network" title=" metabolic network"> metabolic network</a>, <a href="https://publications.waset.org/abstracts/search?q=mixed-integer%20linear%20programming" title=" mixed-integer linear programming"> mixed-integer linear programming</a>, <a href="https://publications.waset.org/abstracts/search?q=multiple%20optimal%20solutions" title=" multiple optimal solutions"> multiple optimal solutions</a> </p> <a href="https://publications.waset.org/abstracts/15698/effect-of-variable-fluxes-on-optimal-flux-distribution-in-a-metabolic-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15698.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">434</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">635</span> An Empirical Study of the Best Fitting Probability Distributions for Stock Returns Modeling</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jayanta%20Pokharel">Jayanta Pokharel</a>, <a href="https://publications.waset.org/abstracts/search?q=Gokarna%20Aryal"> Gokarna Aryal</a>, <a href="https://publications.waset.org/abstracts/search?q=Netra%20Kanaal"> Netra Kanaal</a>, <a href="https://publications.waset.org/abstracts/search?q=Chris%20Tsokos"> Chris Tsokos</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Investment in stocks and shares aims to seek potential gains while weighing the risk of future needs, such as retirement, children's education etc. Analysis of the behavior of the stock market returns and making prediction is important for investors to mitigate risk on investment. Historically, the normal variance models have been used to describe the behavior of stock market returns. However, the returns of the financial assets are actually skewed with higher kurtosis, heavier tails, and a higher center than the normal distribution. The Laplace distribution and its family are natural candidates for modeling stock returns. The Variance-Gamma (VG) distribution is the most sought-after distributions for modeling asset returns and has been extensively discussed in financial literatures. In this paper, it explore the other Laplace family, such as Asymmetric Laplace, Skewed Laplace, Kumaraswamy Laplace (KS) together with Variance-Gamma to model the weekly returns of the S&P 500 Index and it's eleven business sector indices. The method of maximum likelihood is employed to estimate the parameters of the distributions and our empirical inquiry shows that the Kumaraswamy Laplace distribution performs much better for stock returns modeling among the choice of distributions used in this study and in practice, KS can be used as a strong alternative to VG distribution. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=stock%20returns" title="stock returns">stock returns</a>, <a href="https://publications.waset.org/abstracts/search?q=variance-gamma" title=" variance-gamma"> variance-gamma</a>, <a href="https://publications.waset.org/abstracts/search?q=kumaraswamy%20laplace" title=" kumaraswamy laplace"> kumaraswamy laplace</a>, <a href="https://publications.waset.org/abstracts/search?q=maximum%20likelihood" title=" maximum likelihood"> maximum likelihood</a> </p> <a href="https://publications.waset.org/abstracts/174545/an-empirical-study-of-the-best-fitting-probability-distributions-for-stock-returns-modeling" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/174545.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">70</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">634</span> Determination of the Best Fit Probability Distribution for Annual Rainfall in Karkheh River at Iran</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Karim%20Hamidi%20Machekposhti">Karim Hamidi Machekposhti</a>, <a href="https://publications.waset.org/abstracts/search?q=Hossein%20Sedghi"> Hossein Sedghi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study was designed to find the best-fit probability distribution of annual rainfall based on 50 years sample (1966-2015) in the Karkheh river basin at Iran using six probability distributions: Normal, 2-Parameter Log Normal, 3-Parameter Log Normal, Pearson Type 3, Log Pearson Type 3 and Gumbel distribution. The best fit probability distribution was selected using Stormwater Management and Design Aid (SMADA) software and based on the Residual Sum of Squares (R.S.S) between observed and estimated values Based on the R.S.S values of fit tests, the Log Pearson Type 3 and then Pearson Type 3 distributions were found to be the best-fit probability distribution at the Jelogir Majin and Pole Zal rainfall gauging station. The annual values of expected rainfall were calculated using the best fit probability distributions and can be used by hydrologists and design engineers in future research at studied region and other region in the world. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Log%20Pearson%20Type%203" title="Log Pearson Type 3">Log Pearson Type 3</a>, <a href="https://publications.waset.org/abstracts/search?q=SMADA" title=" SMADA"> SMADA</a>, <a href="https://publications.waset.org/abstracts/search?q=rainfall" title=" rainfall"> rainfall</a>, <a href="https://publications.waset.org/abstracts/search?q=Karkheh%20River" title=" Karkheh River"> Karkheh River</a> </p> <a href="https://publications.waset.org/abstracts/97806/determination-of-the-best-fit-probability-distribution-for-annual-rainfall-in-karkheh-river-at-iran" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/97806.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">191</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">633</span> Evaluation of Carbon Dioxide Pressure through Radial Velocity Difference in Arterial Blood Modeled by Drift Flux Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aicha%20Rima%20Cheniti">Aicha Rima Cheniti</a>, <a href="https://publications.waset.org/abstracts/search?q=Hatem%20Besbes"> Hatem Besbes</a>, <a href="https://publications.waset.org/abstracts/search?q=Joseph%20Haggege"> Joseph Haggege</a>, <a href="https://publications.waset.org/abstracts/search?q=Christophe%20Sintes"> Christophe Sintes</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we are interested to determine the carbon dioxide pressure in the arterial blood through radial velocity difference. The blood was modeled as a two phase mixture (an aqueous carbon dioxide solution with carbon dioxide gas) by Drift flux model and the Young-Laplace equation. The distributions of mixture velocities determined from the considered model permitted the calculation of the radial velocity distributions with different values of mean mixture pressure and the calculation of the mean carbon dioxide pressure knowing the mean mixture pressure. The radial velocity distributions are used to deduce a calculation method of the mean mixture pressure through the radial velocity difference between two positions which is measured by ultrasound. The mean carbon dioxide pressure is then deduced from the mean mixture pressure. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mean%20carbon%20dioxide%20pressure" title="mean carbon dioxide pressure">mean carbon dioxide pressure</a>, <a href="https://publications.waset.org/abstracts/search?q=mean%20mixture%20pressure" title=" mean mixture pressure"> mean mixture pressure</a>, <a href="https://publications.waset.org/abstracts/search?q=mixture%20velocity" title=" mixture velocity"> mixture velocity</a>, <a href="https://publications.waset.org/abstracts/search?q=radial%20velocity%20difference" title=" radial velocity difference"> radial velocity difference</a> </p> <a href="https://publications.waset.org/abstracts/51601/evaluation-of-carbon-dioxide-pressure-through-radial-velocity-difference-in-arterial-blood-modeled-by-drift-flux-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51601.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">421</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">632</span> Assessment Using Copulas of Simultaneous Damage to Multiple Buildings Due to Tsunamis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yo%20Fukutani">Yo Fukutani</a>, <a href="https://publications.waset.org/abstracts/search?q=Shuji%20Moriguchi"> Shuji Moriguchi</a>, <a href="https://publications.waset.org/abstracts/search?q=Takuma%20Kotani"> Takuma Kotani</a>, <a href="https://publications.waset.org/abstracts/search?q=Terada%20Kenjiro"> Terada Kenjiro</a> </p> <p class="card-text"><strong>Abstract:</strong></p> If risk management of the assets owned by companies, risk assessment of real estate portfolio, and risk identification of the entire region are to be implemented, it is necessary to consider simultaneous damage to multiple buildings. In this research, the Sagami Trough earthquake tsunami that could have a significant effect on the Japanese capital region is focused on, and a method is proposed for simultaneous damage assessment using copulas that can take into consideration the correlation of tsunami depths and building damage between two sites. First, the tsunami inundation depths at two sites were simulated by using a nonlinear long-wave equation. The tsunamis were simulated by varying the slip amount (five cases) and the depths (five cases) for each of 10 sources of the Sagami Trough. For each source, the frequency distributions of the tsunami inundation depth were evaluated by using the response surface method. Then, Monte-Carlo simulation was conducted, and frequency distributions of tsunami inundation depth were evaluated at the target sites for all sources of the Sagami Trough. These are marginal distributions. Kendall’s tau for the tsunami inundation simulation at two sites was 0.83. Based on this value, the Gaussian copula, t-copula, Clayton copula, and Gumbel copula (n = 10,000) were generated. Then, the simultaneous distributions of the damage rate were evaluated using the marginal distributions and the copulas. For the correlation of the tsunami inundation depth at the two sites, the expected value hardly changed compared with the case of no correlation, but the damage rate of the ninety-ninth percentile value was approximately 2%, and the maximum value was approximately 6% when using the Gumbel copula. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=copulas" title="copulas">copulas</a>, <a href="https://publications.waset.org/abstracts/search?q=Monte-Carlo%20simulation" title=" Monte-Carlo simulation"> Monte-Carlo simulation</a>, <a href="https://publications.waset.org/abstracts/search?q=probabilistic%20risk%20assessment" title=" probabilistic risk assessment"> probabilistic risk assessment</a>, <a href="https://publications.waset.org/abstracts/search?q=tsunamis" title=" tsunamis"> tsunamis</a> </p> <a href="https://publications.waset.org/abstracts/103724/assessment-using-copulas-of-simultaneous-damage-to-multiple-buildings-due-to-tsunamis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/103724.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">631</span> Modelling Volatility of Cryptocurrencies: Evidence from GARCH Family of Models with Skewed Error Innovation Distributions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Timothy%20Kayode%20Samson">Timothy Kayode Samson</a>, <a href="https://publications.waset.org/abstracts/search?q=Adedoyin%20Isola%20Lawal"> Adedoyin Isola Lawal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The past five years have shown a sharp increase in public interest in the crypto market, with its market capitalization growing from $100 billion in June 2017 to $2158.42 billion on April 5, 2022. Despite the outrageous nature of the volatility of cryptocurrencies, the use of skewed error innovation distributions in modelling the volatility behaviour of these digital currencies has not been given much research attention. Hence, this study models the volatility of 5 largest cryptocurrencies by market capitalization (Bitcoin, Ethereum, Tether, Binance coin, and USD Coin) using four variants of GARCH models (GJR-GARCH, sGARCH, EGARCH, and APARCH) estimated using three skewed error innovation distributions (skewed normal, skewed student- t and skewed generalized error innovation distributions). Daily closing prices of these currencies were obtained from Yahoo Finance website. Finding reveals that the Binance coin reported higher mean returns compared to other digital currencies, while the skewness indicates that the Binance coin, Tether, and USD coin increased more than they decreased in values within the period of study. For both Bitcoin and Ethereum, negative skewness was obtained, meaning that within the period of study, the returns of these currencies decreased more than they increased in value. Returns from these cryptocurrencies were found to be stationary but not normality distributed with evidence of the ARCH effect. The skewness parameters in all best forecasting models were all significant (p<.05), justifying of use of skewed error innovation distributions with a fatter tail than normal, Student-t, and generalized error innovation distributions. For Binance coin, EGARCH-sstd outperformed other volatility models, while for Bitcoin, Ethereum, Tether, and USD coin, the best forecasting models were EGARCH-sstd, APARCH-sstd, EGARCH-sged, and GJR-GARCH-sstd, respectively. This suggests the superiority of skewed Student t- distribution and skewed generalized error distribution over the skewed normal distribution. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=skewed%20generalized%20error%20distribution" title="skewed generalized error distribution">skewed generalized error distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=skewed%20normal%20distribution" title=" skewed normal distribution"> skewed normal distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=skewed%20student%20t-%20distribution" title=" skewed student t- distribution"> skewed student t- distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=APARCH" title=" APARCH"> APARCH</a>, <a href="https://publications.waset.org/abstracts/search?q=EGARCH" title=" EGARCH"> EGARCH</a>, <a href="https://publications.waset.org/abstracts/search?q=sGARCH" title=" sGARCH"> sGARCH</a>, <a href="https://publications.waset.org/abstracts/search?q=GJR-GARCH" title=" GJR-GARCH"> GJR-GARCH</a> </p> <a href="https://publications.waset.org/abstracts/151699/modelling-volatility-of-cryptocurrencies-evidence-from-garch-family-of-models-with-skewed-error-innovation-distributions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/151699.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">119</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">630</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">629</span> Exponentiated Transmuted Weibull Distribution: A Generalization of the Weibull Probability Distribution</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abd%20El%20Hady%20N.%20Ebraheim">Abd El Hady N. Ebraheim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper introduces a new generalization of the two parameter Weibull distribution. To this end, the quadratic rank transmutation map has been used. This new distribution is named exponentiated transmuted Weibull (ETW) distribution. The ETW distribution has the advantage of being capable of modeling various shapes of aging and failure criteria. Furthermore, eleven lifetime distributions such as the Weibull, exponentiated Weibull, Rayleigh and exponential distributions, among others follow as special cases. The properties of the new model are discussed and the maximum likelihood estimation is used to estimate the parameters. Explicit expressions are derived for the quantiles. The moments of the distribution are derived, and the order statistics are examined. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=exponentiated" title="exponentiated">exponentiated</a>, <a href="https://publications.waset.org/abstracts/search?q=inversion%20method" title=" inversion method"> inversion method</a>, <a href="https://publications.waset.org/abstracts/search?q=maximum%20likelihood%20estimation" title=" maximum likelihood estimation"> maximum likelihood estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=transmutation%20map" title=" transmutation map"> transmutation map</a> </p> <a href="https://publications.waset.org/abstracts/3470/exponentiated-transmuted-weibull-distribution-a-generalization-of-the-weibull-probability-distribution" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3470.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">565</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">628</span> The Beta-Fisher Snedecor Distribution with Applications to Cancer Remission Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=K.%20A.%20Adepoju">K. A. Adepoju</a>, <a href="https://publications.waset.org/abstracts/search?q=O.%20I.%20Shittu"> O. I. Shittu</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20U.%20Chukwu"> A. U. Chukwu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a new four-parameter generalized version of the Fisher Snedecor distribution called Beta- F distribution is introduced. The comprehensive account of the statistical properties of the new distributions was considered. Formal expressions for the cumulative density function, moments, moment generating function and maximum likelihood estimation, as well as its Fisher information, were obtained. The flexibility of this distribution as well as its robustness using cancer remission time data was demonstrated. The new distribution can be used in most applications where the assumption underlying the use of other lifetime distributions is violated. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fisher-snedecor%20distribution" title="fisher-snedecor distribution">fisher-snedecor distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=beta-f%20distribution" title=" beta-f distribution"> beta-f distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=outlier" title=" outlier"> outlier</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/46554/the-beta-fisher-snedecor-distribution-with-applications-to-cancer-remission-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/46554.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">347</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">627</span> Non-Linear Regression Modeling for Composite Distributions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mostafa%20Aminzadeh">Mostafa Aminzadeh</a>, <a href="https://publications.waset.org/abstracts/search?q=Min%20Deng"> Min Deng</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Modeling loss data is an important part of actuarial science. Actuaries use models to predict future losses and manage financial risk, which can be beneficial for marketing purposes. In the insurance industry, small claims happen frequently while large claims are rare. Traditional distributions such as Normal, Exponential, and inverse-Gaussian are not suitable for describing insurance data, which often show skewness and fat tails. Several authors have studied classical and Bayesian inference for parameters of composite distributions, such as Exponential-Pareto, Weibull-Pareto, and Inverse Gamma-Pareto. These models separate small to moderate losses from large losses using a threshold parameter. This research introduces a computational approach using a nonlinear regression model for loss data that relies on multiple predictors. Simulation studies were conducted to assess the accuracy of the proposed estimation method. The simulations confirmed that the proposed method provides precise estimates for regression parameters. It's important to note that this approach can be applied to datasets if goodness-of-fit tests confirm that the composite distribution under study fits the data well. To demonstrate the computations, a real data set from the insurance industry is analyzed. A Mathematica code uses the Fisher information algorithm as an iteration method to obtain the maximum likelihood estimation (MLE) of regression parameters. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=maximum%20likelihood%20estimation" title="maximum likelihood estimation">maximum likelihood estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=fisher%20scoring%20method" title=" fisher scoring method"> fisher scoring method</a>, <a href="https://publications.waset.org/abstracts/search?q=non-linear%20regression%20models" title=" non-linear regression models"> non-linear regression models</a>, <a href="https://publications.waset.org/abstracts/search?q=composite%20distributions" title=" composite distributions"> composite distributions</a> </p> <a href="https://publications.waset.org/abstracts/189035/non-linear-regression-modeling-for-composite-distributions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/189035.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">34</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=distributions&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=distributions&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=distributions&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=distributions&page=5">5</a></li> <li class="page-item"><a class="page-link" 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