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generalized logistic distribution</title> <meta name="description" content="Search results for: type II generalized logistic distribution"> <meta name="keywords" content="type II generalized logistic distribution"> <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 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style="font-size:1.6rem;">Search results for: type II generalized logistic distribution</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12632</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">12631</span> Classical and Bayesian Inference of the Generalized Log-Logistic Distribution with Applications to Survival Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abdisalam%20Hassan%20Muse">Abdisalam Hassan Muse</a>, <a href="https://publications.waset.org/abstracts/search?q=Samuel%20Mwalili"> Samuel Mwalili</a>, <a href="https://publications.waset.org/abstracts/search?q=Oscar%20Ngesa"> Oscar Ngesa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A generalized log-logistic distribution with variable shapes of the hazard rate was introduced and studied, extending the log-logistic distribution by adding an extra parameter to the classical distribution, leading to greater flexibility in analysing and modeling various data types. The proposed distribution has a large number of well-known lifetime special sub-models such as; Weibull, log-logistic, exponential, and Burr XII distributions. Its basic mathematical and statistical properties were derived. The method of maximum likelihood was adopted for estimating the unknown parameters of the proposed distribution, and a Monte Carlo simulation study is carried out to assess the behavior of the estimators. The importance of this distribution is that its tendency to model both monotone (increasing and decreasing) and non-monotone (unimodal and bathtub shape) or reversed “bathtub” shape hazard rate functions which are quite common in survival and reliability data analysis. Furthermore, the flexibility and usefulness of the proposed distribution are illustrated in a real-life data set and compared to its sub-models; Weibull, log-logistic, and BurrXII distributions and other parametric survival distributions with 3-parmaeters; like the exponentiated Weibull distribution, the 3-parameter lognormal distribution, the 3- parameter gamma distribution, the 3-parameter Weibull distribution, and the 3-parameter log-logistic (also known as shifted log-logistic) distribution. The proposed distribution provided a better fit than all of the competitive distributions based on the goodness-of-fit tests, the log-likelihood, and information criterion values. Finally, Bayesian analysis and performance of Gibbs sampling for the data set are also carried out. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hazard%20rate%20function" title="hazard rate function">hazard rate function</a>, <a href="https://publications.waset.org/abstracts/search?q=log-logistic%20distribution" title=" log-logistic distribution"> log-logistic distribution</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=generalized%20log-logistic%20distribution" title=" generalized log-logistic distribution"> generalized log-logistic distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=survival%20data" title=" survival data"> survival data</a>, <a href="https://publications.waset.org/abstracts/search?q=Monte%20Carlo%20simulation" title=" Monte Carlo simulation"> Monte Carlo simulation</a> </p> <a href="https://publications.waset.org/abstracts/139326/classical-and-bayesian-inference-of-the-generalized-log-logistic-distribution-with-applications-to-survival-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/139326.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">202</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">12630</span> Generalized Extreme Value Regression with Binary Dependent Variable: An Application for Predicting Meteorological Drought Probabilities</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Retius%20Chifurira">Retius Chifurira</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Logistic regression model is the most used regression model to predict meteorological drought probabilities. When the dependent variable is extreme, the logistic model fails to adequately capture drought probabilities. In order to adequately predict drought probabilities, we use the generalized linear model (GLM) with the quantile function of the generalized extreme value distribution (GEVD) as the link function. The method maximum likelihood estimation is used to estimate the parameters of the generalized extreme value (GEV) regression model. We compare the performance of the logistic and the GEV regression models in predicting drought probabilities for Zimbabwe. The performance of the regression models are assessed using the goodness-of-fit tests, namely; relative root mean square error (RRMSE) and relative mean absolute error (RMAE). Results show that the GEV regression model performs better than the logistic model, thereby providing a good alternative candidate for predicting drought probabilities. This paper provides the first application of GLM derived from extreme value theory to predict drought probabilities for a drought-prone country such as Zimbabwe. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=generalized%20extreme%20value%20distribution" title="generalized extreme value distribution">generalized extreme value distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=general%20linear%20model" title=" general linear model"> general linear model</a>, <a href="https://publications.waset.org/abstracts/search?q=mean%20annual%20rainfall" title=" mean annual rainfall"> mean annual rainfall</a>, <a href="https://publications.waset.org/abstracts/search?q=meteorological%20drought%20probabilities" title=" meteorological drought probabilities"> meteorological drought probabilities</a> </p> <a href="https://publications.waset.org/abstracts/99321/generalized-extreme-value-regression-with-binary-dependent-variable-an-application-for-predicting-meteorological-drought-probabilities" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/99321.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">200</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">12629</span> An Extension of the Generalized Extreme Value Distribution</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Serge%20Provost">Serge Provost</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdous%20Saboor"> Abdous Saboor</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A q-analogue of the generalized extreme value distribution which includes the Gumbel distribution is introduced. The additional parameter q allows for increased modeling flexibility. The resulting distribution can have a finite, semi-infinite or infinite support. It can also produce several types of hazard rate functions. The model parameters are determined by making use of the method of maximum likelihood. It will be shown that it compares favourably to three related distributions in connection with the modeling of a certain hydrological data set. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=extreme%20value%20theory" title="extreme value theory">extreme value theory</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20extreme%20value%20distribution" title=" generalized extreme value distribution"> generalized extreme value distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=goodness-of-fit%20statistics" title=" goodness-of-fit statistics"> goodness-of-fit statistics</a>, <a href="https://publications.waset.org/abstracts/search?q=Gumbel%20distribution" title=" Gumbel distribution"> Gumbel distribution</a> </p> <a href="https://publications.waset.org/abstracts/72656/an-extension-of-the-generalized-extreme-value-distribution" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72656.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">12628</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">511</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">12627</span> On Fourier Type Integral Transform for a Class of Generalized Quotients</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20S.%20Issa">A. S. Issa</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20K.%20Q.%20AL-Omari"> S. K. Q. AL-Omari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we investigate certain spaces of generalized functions for the Fourier and Fourier type integral transforms. We discuss convolution theorems and establish certain spaces of distributions for the considered integrals. The new Fourier type integral is well-defined, linear, one-to-one and continuous with respect to certain types of convergences. Many properties and an inverse problem are also discussed in some details. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Boehmian" title="Boehmian">Boehmian</a>, <a href="https://publications.waset.org/abstracts/search?q=Fourier%20integral" title=" Fourier integral"> Fourier integral</a>, <a href="https://publications.waset.org/abstracts/search?q=Fourier%20type%20integral" title=" Fourier type integral"> Fourier type integral</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20quotient" title=" generalized quotient"> generalized quotient</a> </p> <a href="https://publications.waset.org/abstracts/45947/on-fourier-type-integral-transform-for-a-class-of-generalized-quotients" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45947.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">365</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">12626</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">12625</span> A Kolmogorov-Smirnov Type Goodness-Of-Fit Test of Multinomial Logistic Regression Model in Case-Control Studies</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chen%20Li-Ching">Chen Li-Ching </a> </p> <p class="card-text"><strong>Abstract:</strong></p> The multinomial logistic regression model is used popularly for inferring the relationship of risk factors and disease with multiple categories. This study based on the discrepancy between the nonparametric maximum likelihood estimator and semiparametric maximum likelihood estimator of the cumulative distribution function to propose a Kolmogorov-Smirnov type test statistic to assess adequacy of the multinomial logistic regression model for case-control data. A bootstrap procedure is presented to calculate the critical value of the proposed test statistic. Empirical type I error rates and powers of the test are performed by simulation studies. Some examples will be illustrated the implementation of the test. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=case-control%20studies" title="case-control studies">case-control studies</a>, <a href="https://publications.waset.org/abstracts/search?q=goodness-of-fit%20test" title=" goodness-of-fit test"> goodness-of-fit test</a>, <a href="https://publications.waset.org/abstracts/search?q=Kolmogorov-Smirnov%20test" title=" Kolmogorov-Smirnov test"> Kolmogorov-Smirnov test</a>, <a href="https://publications.waset.org/abstracts/search?q=multinomial%20logistic%20regression" title=" multinomial logistic regression"> multinomial logistic regression</a> </p> <a href="https://publications.waset.org/abstracts/44967/a-kolmogorov-smirnov-type-goodness-of-fit-test-of-multinomial-logistic-regression-model-in-case-control-studies" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/44967.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">456</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">12624</span> Confidence Intervals for Quantiles in the Two-Parameter Exponential Distributions with Type II Censored Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ayman%20Baklizi">Ayman Baklizi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Based on type II censored data, we consider interval estimation of the quantiles of the two-parameter exponential distribution and the difference between the quantiles of two independent two-parameter exponential distributions. We derive asymptotic intervals, Bayesian, as well as intervals based on the generalized pivot variable. We also include some bootstrap intervals in our comparisons. The performance of these intervals is investigated in terms of their coverage probabilities and expected lengths. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=asymptotic%20intervals" title="asymptotic intervals">asymptotic intervals</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayes%20intervals" title=" Bayes intervals"> Bayes intervals</a>, <a href="https://publications.waset.org/abstracts/search?q=bootstrap" title=" bootstrap"> bootstrap</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20pivot%20variables" title=" generalized pivot variables"> generalized pivot variables</a>, <a href="https://publications.waset.org/abstracts/search?q=two-parameter%20exponential%20distribution" title=" two-parameter exponential distribution"> two-parameter exponential distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=quantiles" title=" quantiles"> quantiles</a> </p> <a href="https://publications.waset.org/abstracts/28592/confidence-intervals-for-quantiles-in-the-two-parameter-exponential-distributions-with-type-ii-censored-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/28592.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">415</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">12623</span> Weyl Type Theorem and the Fuglede Property</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20H.%20M.%20Rashid">M. H. M. Rashid</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Given H a Hilbert space and B(H) the algebra of bounded linear operator in H, let δAB denote the generalized derivation defined by A and B. The main objective of this article is to study Weyl type theorems for generalized derivation for (A,B) satisfying a couple of Fuglede. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fuglede%20Property" title="Fuglede Property">Fuglede Property</a>, <a href="https://publications.waset.org/abstracts/search?q=Weyl%E2%80%99s%20theorem" title=" Weyl’s theorem"> Weyl’s theorem</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20derivation" title=" generalized derivation"> generalized derivation</a>, <a href="https://publications.waset.org/abstracts/search?q=Aluthge%20transform" title=" Aluthge transform"> Aluthge transform</a> </p> <a href="https://publications.waset.org/abstracts/113531/weyl-type-theorem-and-the-fuglede-property" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/113531.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">128</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">12622</span> Modeling of Maximum Rainfall Using Poisson-Generalized Pareto Distribution in Kigali, Rwanda</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Emmanuel%20Iyamuremye">Emmanuel Iyamuremye</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Extreme rainfall events have caused significant damage to agriculture, ecology, and infrastructure, disruption of human activities, injury, and loss of life. They also have significant social, economic, and environmental consequences because they considerably damage urban as well as rural areas. Early detection of extreme maximum rainfall helps to implement strategies and measures, before they occur, hence mitigating the consequences. Extreme value theory has been used widely in modeling extreme rainfall and in various disciplines, such as financial markets, the insurance industry, failure cases. Climatic extremes have been analyzed by using either generalized extreme value (GEV) or generalized Pareto (GP) distributions, which provides evidence of the importance of modeling extreme rainfall from different regions of the world. In this paper, we focused on Peak Over Thresholds approach, where the Poisson-generalized Pareto distribution is considered as the proper distribution for the study of the exceedances. This research also considers the use of the generalized Pareto (GP) distribution with a Poisson model for arrivals to describe peaks over a threshold. The research used statistical techniques to fit models that used to predict extreme rainfall in Kigali. The results indicate that the proposed Poisson-GP distribution provides a better fit to maximum monthly rainfall data. Further, the Poisson-GP models are able to estimate various return levels. The research also found a slow increase in return levels for maximum monthly rainfall for higher return periods, and further, the intervals are increasingly wider as the return period is increasing. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=exceedances" title="exceedances">exceedances</a>, <a href="https://publications.waset.org/abstracts/search?q=extreme%20value%20theory" title=" extreme value theory"> extreme value theory</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20Pareto%20distribution" title=" generalized Pareto distribution"> generalized Pareto distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=Poisson%20generalized%20Pareto%20distribution" title=" Poisson generalized Pareto distribution"> Poisson generalized Pareto distribution</a> </p> <a href="https://publications.waset.org/abstracts/127379/modeling-of-maximum-rainfall-using-poisson-generalized-pareto-distribution-in-kigali-rwanda" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/127379.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">136</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">12621</span> Durrmeyer Type Modification of q-Generalized Bernstein Operators</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ruchi">Ruchi</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20M.%20Acu"> A. M. Acu</a>, <a href="https://publications.waset.org/abstracts/search?q=Purshottam%20N.%20Agrawal"> Purshottam N. Agrawal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The purpose of this paper to introduce the Durrmeyer type modification of q-generalized-Bernstein operators which include the Bernstein polynomials in the particular α = 0. We investigate the rate of convergence by means of the Lipschitz class and the Peetre’s K-functional. Also, we define the bivariate case of Durrmeyer type modification of q-generalized-Bernstein operators and study the degree of approximation with the aid of the partial modulus of continuity and the Peetre’s K-functional. Finally, we introduce the GBS (Generalized Boolean Sum) of the Durrmeyer type modification of q- generalized-Bernstein operators and investigate the approximation of the Bögel continuous and Bögel differentiable functions with the aid of the Lipschitz class and the mixed modulus of smoothness. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=B%C3%B6gel%20continuous" title="Bögel continuous">Bögel continuous</a>, <a href="https://publications.waset.org/abstracts/search?q=B%C3%B6gel%20differentiable" title=" Bögel differentiable"> Bögel differentiable</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20Boolean%20sum" title=" generalized Boolean sum"> generalized Boolean sum</a>, <a href="https://publications.waset.org/abstracts/search?q=Peetre%E2%80%99s%20K-functional" title=" Peetre’s K-functional"> Peetre’s K-functional</a>, <a href="https://publications.waset.org/abstracts/search?q=Lipschitz%20class" title=" Lipschitz class"> Lipschitz class</a>, <a href="https://publications.waset.org/abstracts/search?q=mixed%20modulus%20of%20smoothness" title=" mixed modulus of smoothness"> mixed modulus of smoothness</a> </p> <a href="https://publications.waset.org/abstracts/79175/durrmeyer-type-modification-of-q-generalized-bernstein-operators" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/79175.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">213</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">12620</span> VaR or TCE: Explaining the Preferences of Regulators</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Silvia%20Faroni">Silvia Faroni</a>, <a href="https://publications.waset.org/abstracts/search?q=Olivier%20Le%20Courtois"> Olivier Le Courtois</a>, <a href="https://publications.waset.org/abstracts/search?q=Krzysztof%20Ostaszewski"> Krzysztof Ostaszewski</a> </p> <p class="card-text"><strong>Abstract:</strong></p> While a lot of research concentrates on the merits of VaR and TCE, which are the two most classic risk indicators used by financial institutions, little has been written on explaining why regulators favor the choice of VaR or TCE in their set of rules. In this paper, we investigate the preferences of regulators with the aim of understanding why, for instance, a VaR with a given confidence level is ultimately retained. Further, this paper provides equivalence rules that explain how a given choice of VaR can be equivalent to a given choice of TCE. Then, we introduce a new risk indicator that extends TCE by providing a more versatile weighting of the constituents of probability distribution tails. All of our results are illustrated using the generalized Pareto distribution. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=generalized%20pareto%20distribution" title="generalized pareto distribution">generalized pareto distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20tail%20conditional%20expectation" title=" generalized tail conditional expectation"> generalized tail conditional expectation</a>, <a href="https://publications.waset.org/abstracts/search?q=regulator%20preferences" title=" regulator preferences"> regulator preferences</a>, <a href="https://publications.waset.org/abstracts/search?q=risk%20measure" title=" risk measure"> risk measure</a> </p> <a href="https://publications.waset.org/abstracts/146694/var-or-tce-explaining-the-preferences-of-regulators" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/146694.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">172</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">12619</span> An Efficient Discrete Chaos in Generalized Logistic Maps with Applications in Image Encryption </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ashish%20Ashish">Ashish Ashish</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the last few decades, the discrete chaos of difference equations has gained a massive attention of academicians and scholars due to its tremendous applications in each and every branch of science, such as cryptography, traffic control models, secure communications, weather forecasting, and engineering. In this article, a generalized logistic discrete map is established and discrete chaos is reported through period doubling bifurcation, period three orbit and Lyapunov exponent. It is interesting to see that the generalized logistic map exhibits superior chaos due to the presence of an extra degree of freedom of an ordered parameter. The period doubling bifurcation and Lyapunov exponent are demonstrated for some particular values of parameter and the discrete chaos is determined in the sense of Devaney's definition of chaos theoretically as well as numerically. Moreover, the study discusses an extended chaos based image encryption and decryption scheme in cryptography using this novel system. Surprisingly, a larger key space for coding and more sensitive dependence on initial conditions are examined for encryption and decryption of text messages, images and videos which secure the system strongly from external cyber attacks, coding attacks, statistic attacks and differential attacks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=chaos" title="chaos">chaos</a>, <a href="https://publications.waset.org/abstracts/search?q=period-doubling" title=" period-doubling"> period-doubling</a>, <a href="https://publications.waset.org/abstracts/search?q=logistic%20map" title=" logistic map"> logistic map</a>, <a href="https://publications.waset.org/abstracts/search?q=Lyapunov%20exponent" title=" Lyapunov exponent"> Lyapunov exponent</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20encryption" title=" image encryption"> image encryption</a> </p> <a href="https://publications.waset.org/abstracts/112408/an-efficient-discrete-chaos-in-generalized-logistic-maps-with-applications-in-image-encryption" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/112408.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">154</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">12618</span> Generalized Additive Model for Estimating Propensity Score</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tahmidul%20Islam">Tahmidul Islam</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Propensity Score Matching (PSM) technique has been widely used for estimating causal effect of treatment in observational studies. One major step of implementing PSM is estimating the propensity score (PS). Logistic regression model with additive linear terms of covariates is most used technique in many studies. Logistics regression model is also used with cubic splines for retaining flexibility in the model. However, choosing the functional form of the logistic regression model has been a question since the effectiveness of PSM depends on how accurately the PS been estimated. In many situations, the linearity assumption of linear logistic regression may not hold and non-linear relation between the logit and the covariates may be appropriate. One can estimate PS using machine learning techniques such as random forest, neural network etc for more accuracy in non-linear situation. In this study, an attempt has been made to compare the efficacy of Generalized Additive Model (GAM) in various linear and non-linear settings and compare its performance with usual logistic regression. GAM is a non-parametric technique where functional form of the covariates can be unspecified and a flexible regression model can be fitted. In this study various simple and complex models have been considered for treatment under several situations (small/large sample, low/high number of treatment units) and examined which method leads to more covariate balance in the matched dataset. It is found that logistic regression model is impressively robust against inclusion quadratic and interaction terms and reduces mean difference in treatment and control set equally efficiently as GAM does. GAM provided no significantly better covariate balance than logistic regression in both simple and complex models. The analysis also suggests that larger proportion of controls than treatment units leads to better balance for both of the methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=accuracy" title="accuracy">accuracy</a>, <a href="https://publications.waset.org/abstracts/search?q=covariate%20balances" title=" covariate balances"> covariate balances</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20additive%20model" title=" generalized additive model"> generalized additive model</a>, <a href="https://publications.waset.org/abstracts/search?q=logistic%20regression" title=" logistic regression"> logistic regression</a>, <a href="https://publications.waset.org/abstracts/search?q=non-linearity" title=" non-linearity"> non-linearity</a>, <a href="https://publications.waset.org/abstracts/search?q=propensity%20score%20matching" title=" propensity score matching"> propensity score matching</a> </p> <a href="https://publications.waset.org/abstracts/40433/generalized-additive-model-for-estimating-propensity-score" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/40433.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">368</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">12617</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">12616</span> Frequency Analysis of Minimum Ecological Flow and Gage Height in Indus River Using Maximum Likelihood Estimation</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%20Wan"> Yejuan Wan</a>, <a href="https://publications.waset.org/abstracts/search?q=Kalim%20Ullah"> Kalim Ullah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Hydrological frequency analysis has been conducted to estimate the minimum flow elevation of the Indus River in Pakistan to protect the ecosystem. The Maximum likelihood estimation (MLE) technique is used to estimate the best-fitted distribution for Minimum Ecological Flows at nine stations of the Indus River in Pakistan. The four selected distributions, Generalized Extreme Value (GEV) distribution, Generalized Logistics (GLO) distribution, Generalized Pareto (GPA) distribution, and Pearson type 3 (PE3) are fitted in all sites, usually used in hydro frequency analysis. Compare the performance of these distributions by using the goodness of fit tests, such as the Kolmogorov Smirnov test, Anderson darling test, and chi-square test. The study concludes that the Maximum Likelihood Estimation (MLE) method recommended that GEV and GPA are the most suitable distributions which can be effectively applied to all the proposed sites. The quantiles are estimated for the return periods from 5 to 1000 years by using MLE, estimations methods. The MLE is the robust method for larger sample sizes. The results of these analyses can be used for water resources research, including water quality management, designing irrigation systems, determining downstream flow requirements for hydropower, and the impact of long-term drought on the country's aquatic system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=minimum%20ecological%20flow" title="minimum ecological flow">minimum ecological flow</a>, <a href="https://publications.waset.org/abstracts/search?q=frequency%20distribution" title=" frequency distribution"> frequency distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=indus%20river" title=" indus river"> indus river</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/161795/frequency-analysis-of-minimum-ecological-flow-and-gage-height-in-indus-river-using-maximum-likelihood-estimation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/161795.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">77</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">12615</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">12614</span> On the Analysis of Strategies of Buechi Games</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ahmad%20Termimi%20Ab%20Ghani">Ahmad Termimi Ab Ghani</a>, <a href="https://publications.waset.org/abstracts/search?q=Kojiro%20Higuchi"> Kojiro Higuchi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present some results of simultaneous infinite games. We mainly work with generalized reachability games and Buechi games. These games are two-player concurrent games where each player chooses simultaneously their moves at each step. Our goal is to give simple expressions of values for each game. Moreover, we are interested in the question of what type of optimal (ε-optimal) strategy exists for both players depending on the type of games. We first show the determinacy (optimal value) and optimal (ε-optimal) strategies in generalized reachability games. We provide a simple expressions of value of this game and prove the existence of memoryless randomized ε-optimal strategy for Player I in any generalized reachability games. We then observe games with more complex objectives, games with Buechi objectives. We present how to compute an ε-optimal strategies and approximate a value of game in some way. Specifically, the results of generalized reachability games are used to show the value of Buechi games can be approximated as values of some generalized reachability games. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=optimal%20Strategies" title="optimal Strategies">optimal Strategies</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20reachability%20games" title=" generalized reachability games"> generalized reachability games</a>, <a href="https://publications.waset.org/abstracts/search?q=Buechi%20games" title=" Buechi games"> Buechi games</a> </p> <a href="https://publications.waset.org/abstracts/23287/on-the-analysis-of-strategies-of-buechi-games" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/23287.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">593</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">12613</span> Time Truncated Group Acceptance Sampling Plans for Exponentiated Half Logistic Distribution</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Srinivasa%20Rao%20Gadde">Srinivasa Rao Gadde</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this article, we considered a group acceptance sampling plans for exponentiated half logistic distribution when the life-test is truncated at a pre-specified time. It is assumed that the index parameter of the exponentiated half logistic distribution is known. The design parameters such as the number of groups and the acceptance number are obtained by satisfying the producer’s and consumer’s risks at the specified quality levels in terms of medians and 10th percentiles under the assumption that the termination time and the number of items in each group are pre-fixed. Finally, an example is given to illustration the methodology. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=group%20acceptance%20sampling%20plan" title="group acceptance sampling plan">group acceptance sampling plan</a>, <a href="https://publications.waset.org/abstracts/search?q=operating%20characteristic" title=" operating characteristic"> operating characteristic</a>, <a href="https://publications.waset.org/abstracts/search?q=consumer%20and%20producer%E2%80%99s%20risks" title=" consumer and producer’s risks"> consumer and producer’s risks</a>, <a href="https://publications.waset.org/abstracts/search?q=truncated%20life-test" title=" truncated life-test"> truncated life-test</a> </p> <a href="https://publications.waset.org/abstracts/14790/time-truncated-group-acceptance-sampling-plans-for-exponentiated-half-logistic-distribution" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14790.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">341</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">12612</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">350</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">12611</span> Approximation by Generalized Lupaş-Durrmeyer Operators with Two Parameter α and β</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Preeti%20Sharma">Preeti Sharma</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper deals with the Stancu type generalization of Lupaş-Durrmeyer operators. We establish some direct results in the polynomial weighted space of continuous functions defined on the interval [0, 1]. Also, Voronovskaja type theorem is studied. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lupas-Durrmeyer%20operators" title="Lupas-Durrmeyer operators">Lupas-Durrmeyer operators</a>, <a href="https://publications.waset.org/abstracts/search?q=polya%20distribution" title=" polya distribution"> polya distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=weighted%20approximation" title=" weighted approximation"> weighted approximation</a>, <a href="https://publications.waset.org/abstracts/search?q=rate%20of%20convergence" title=" rate of convergence"> rate of convergence</a>, <a href="https://publications.waset.org/abstracts/search?q=modulus%20of%20continuity" title=" modulus of continuity"> modulus of continuity</a> </p> <a href="https://publications.waset.org/abstracts/47660/approximation-by-generalized-lupas-durrmeyer-operators-with-two-parameter-a-and-v" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/47660.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">346</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12610</span> Developing a Cybernetic Model of Interdepartmental Logistic Interactions in SME</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jonas%20Mayer">Jonas Mayer</a>, <a href="https://publications.waset.org/abstracts/search?q=Kai-Frederic%20Seitz"> Kai-Frederic Seitz</a>, <a href="https://publications.waset.org/abstracts/search?q=Thorben%20Kuprat"> Thorben Kuprat</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In today’s competitive environment production’s logistic objectives such as ‘delivery reliability’ and ‘delivery time’ and distribution’s logistic objectives such as ‘service level’ and ‘delivery delay’ are attributed great importance. Especially for small and mid-sized enterprises (SME) attaining these objectives pose a key challenge. Within this context, one of the difficulties is that interactions between departments within the enterprise and their specific objectives are insufficiently taken into account and aligned. Interdepartmental independencies along with contradicting targets set within the different departments result in enterprises having sub-optimal logistic performance capability. This paper presents a research project which will systematically describe the interactions between departments and convert them into a quantifiable form. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=department-specific%20actuating%20and%20control%20variables" title="department-specific actuating and control variables">department-specific actuating and control variables</a>, <a href="https://publications.waset.org/abstracts/search?q=interdepartmental%20interactions" title=" interdepartmental interactions"> interdepartmental interactions</a>, <a href="https://publications.waset.org/abstracts/search?q=cybernetic%20model" title=" cybernetic model"> cybernetic model</a>, <a href="https://publications.waset.org/abstracts/search?q=logistic%20objectives" title=" logistic objectives"> logistic objectives</a> </p> <a href="https://publications.waset.org/abstracts/10592/developing-a-cybernetic-model-of-interdepartmental-logistic-interactions-in-sme" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10592.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">372</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">12609</span> Pressure Distribution, Load Capacity, and Thermal Effect with Generalized Maxwell Model in Journal Bearing Lubrication</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Guemmadi">M. Guemmadi</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Ouibrahim"> A. Ouibrahim </a> </p> <p class="card-text"><strong>Abstract:</strong></p> This numerical investigation aims to evaluate how a viscoelastic lubricant described by a generalized Maxwell model, affects the pressure distribution, the load capacity and thermal effect in a journal bearing lubrication. We use for the purpose the CFD package software completed by adapted user define functions (UDFs) to solve the coupled equations of momentum, of energy and of the viscoelastic model (generalized Maxwell model). Two parameters, viscosity and relaxation time are involved to show how viscoelasticity substantially affect the pressure distribution, the load capacity and the thermal transfer by comparison to Newtonian lubricant. These results were also compared with the available published results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=journal%20bearing" title="journal bearing">journal bearing</a>, <a href="https://publications.waset.org/abstracts/search?q=lubrication" title=" lubrication"> lubrication</a>, <a href="https://publications.waset.org/abstracts/search?q=Maxwell%20model" title=" Maxwell model"> Maxwell model</a>, <a href="https://publications.waset.org/abstracts/search?q=viscoelastic%20fluids" title=" viscoelastic fluids"> viscoelastic fluids</a>, <a href="https://publications.waset.org/abstracts/search?q=computational%20modelling" title=" computational modelling"> computational modelling</a>, <a href="https://publications.waset.org/abstracts/search?q=load%20capacity" title=" load capacity"> load capacity</a> </p> <a href="https://publications.waset.org/abstracts/13167/pressure-distribution-load-capacity-and-thermal-effect-with-generalized-maxwell-model-in-journal-bearing-lubrication" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13167.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">542</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">12608</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">12607</span> Evaluation of Best-Fit Probability Distribution for Prediction of Extreme Hydrologic Phenomena</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> The probability distributions are the best method for forecasting of extreme hydrologic phenomena such as rainfall and flood flows. In this research, in order to determine suitable probability distribution for estimating of annual extreme rainfall and flood flows (discharge) series with different return periods, precipitation with 40 and discharge with 58 years time period had been collected from Karkheh River at Iran. After homogeneity and adequacy tests, data have been analyzed by Stormwater Management and Design Aid (SMADA) software and residual sum of squares (R.S.S). The best probability distribution was Log Pearson Type III with R.S.S value (145.91) and value (13.67) for peak discharge and Log Pearson Type III with R.S.S values (141.08) and (8.95) for maximum discharge in Jelogir Majin and Pole Zal stations, respectively. The best distribution for maximum precipitation in Jelogir Majin and Pole Zal stations was Log Pearson Type III distribution with R.S.S values (1.74&1.90) and then Pearson Type III distribution with R.S.S values (1.53&1.69). Overall, the Log Pearson Type III distributions are acceptable distribution types for representing statistics of extreme hydrologic phenomena in Karkheh River at Iran with the Pearson Type III distribution as a potential alternative. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Karkheh%20River" title="Karkheh River">Karkheh River</a>, <a href="https://publications.waset.org/abstracts/search?q=Log%20Pearson%20Type%20III" title=" Log Pearson Type III"> Log Pearson Type III</a>, <a href="https://publications.waset.org/abstracts/search?q=probability%20distribution" title=" probability distribution"> probability distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=residual%20sum%20of%20squares" title=" residual sum of squares"> residual sum of squares</a> </p> <a href="https://publications.waset.org/abstracts/97779/evaluation-of-best-fit-probability-distribution-for-prediction-of-extreme-hydrologic-phenomena" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/97779.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">12606</span> Parametric Modeling for Survival Data with Competing Risks Using the Generalized Gompertz Distribution</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Noora%20Al-Shanfari">Noora Al-Shanfari</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Mazharul%20Islam"> M. Mazharul Islam</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The cumulative incidence function (CIF) is a fundamental approach for analyzing survival data in the presence of competing risks, which estimates the marginal probability for each competing event. Parametric modeling of CIF has the advantage of fitting various shapes of CIF and estimates the impact of covariates with maximum efficiency. To calculate the total CIF's covariate influence using a parametric model., it is essential to parametrize the baseline of the CIF. As the CIF is an improper function by nature, it is necessary to utilize an improper distribution when applying parametric models. The Gompertz distribution, which is an improper distribution, is limited in its applicability as it only accounts for monotone hazard shapes. The generalized Gompertz distribution, however, can adapt to a wider range of hazard shapes, including unimodal, bathtub, and monotonic increasing or decreasing hazard shapes. In this paper, the generalized Gompertz distribution is used to parametrize the baseline of the CIF, and the parameters of the proposed model are estimated using the maximum likelihood approach. The proposed model is compared with the existing Gompertz model using the Akaike information criterion. Appropriate statistical test procedures and model-fitting criteria will be used to test the adequacy of the model. Both models are applied to the ‘colon’ dataset, which is available in the “biostat3” package in R. <p class="card-text"><strong>Keywords:</strong> <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=cumulative%20incidence%20function" title=" cumulative incidence function"> cumulative incidence function</a>, <a href="https://publications.waset.org/abstracts/search?q=improper%20distribution" title=" improper distribution"> improper distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=parametric%20modeling" title=" parametric modeling"> parametric modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=survival%20analysis" title=" survival analysis"> survival analysis</a> </p> <a href="https://publications.waset.org/abstracts/162228/parametric-modeling-for-survival-data-with-competing-risks-using-the-generalized-gompertz-distribution" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/162228.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">104</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12605</span> Logistics Information Systems in the Distribution of Flour in Nigeria</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Cornelius%20Femi%20Popoola">Cornelius Femi Popoola</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study investigated logistics information systems in the distribution of flour in Nigeria. A case study design was used and 50 staff of Honeywell Flour Mill was sampled for the study. Data generated through a questionnaire were analysed using correlation and regression analysis. The findings of the study revealed that logistic information systems such as e-commerce, interactive telephone systems and electronic data interchange positively correlated with the distribution of flour in Honeywell Flour Mill. Finding also deduced that e-commerce, interactive telephone systems and electronic data interchange jointly and positively contribute to the distribution of flour in Honeywell Flour Mill in Nigeria (R = .935; Adj. R2 = .642; F (3,47) = 14.739; p < .05). The study therefore recommended that Honeywell Flour Mill should upgrade their logistic information systems to computer-to-computer communication of business transactions and documents, as well adopt new technology such as, tracking-and-tracing systems (barcode scanning for packages and palettes), tracking vehicles with Global Positioning System (GPS), measuring vehicle performance with ‘black boxes’ (containing logistic data), and Automatic Equipment Identification (AEI) into their systems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=e-commerce" title="e-commerce">e-commerce</a>, <a href="https://publications.waset.org/abstracts/search?q=electronic%20data%20interchange" title=" electronic data interchange"> electronic data interchange</a>, <a href="https://publications.waset.org/abstracts/search?q=flour%20distribution" title=" flour distribution"> flour distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20system" title=" information system"> information system</a>, <a href="https://publications.waset.org/abstracts/search?q=interactive%20telephone%20systems" title=" interactive telephone systems"> interactive telephone systems</a> </p> <a href="https://publications.waset.org/abstracts/21781/logistics-information-systems-in-the-distribution-of-flour-in-nigeria" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21781.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">553</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">12604</span> Kinetic Model to Interpret Whistler Waves in Multicomponent Non-Maxwellian Space Plasmas</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Warda%20Nasir">Warda Nasir</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20N.%20S.%20Qureshi"> M. N. S. Qureshi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Whistler waves are right handed circularly polarized waves and are frequently observed in space plasmas. The Low frequency branch of the Whistler waves having frequencies nearly around 100 Hz, known as Lion roars, are frequently observed in magnetosheath. Another feature of the magnetosheath is the observations of flat top electron distributions with single as well as two electron populations. In the past, lion roars were studied by employing kinetic model using classical bi-Maxwellian distribution function, however, could not be justified both on quantitatively as well as qualitatively grounds. We studied Whistler waves by employing kinetic model using non-Maxwellian distribution function such as the generalized (r,q) distribution function which is the generalized form of kappa and Maxwellian distribution functions by employing kinetic theory with single or two electron populations. We compare our results with the Cluster observations and found good quantitative and qualitative agreement between them. At times when lion roars are observed (not observed) in the data and bi-Maxwellian could not provide the sufficient growth (damping) rates, we showed that when generalized (r,q) distribution function is employed, the resulted growth (damping) rates exactly match the observations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=kinetic%20model" title="kinetic model">kinetic model</a>, <a href="https://publications.waset.org/abstracts/search?q=whistler%20waves" title=" whistler waves"> whistler waves</a>, <a href="https://publications.waset.org/abstracts/search?q=non-maxwellian%20distribution%20function" title=" non-maxwellian distribution function"> non-maxwellian distribution function</a>, <a href="https://publications.waset.org/abstracts/search?q=space%20plasmas" title=" space plasmas"> space plasmas</a> </p> <a href="https://publications.waset.org/abstracts/52048/kinetic-model-to-interpret-whistler-waves-in-multicomponent-non-maxwellian-space-plasmas" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52048.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">314</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">12603</span> A Two-Pronged Truncated Deferred Sampling Plan for Log-Logistic Distribution</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Braimah%20Joseph%20Odunayo">Braimah Joseph Odunayo</a>, <a href="https://publications.waset.org/abstracts/search?q=Jiju%20Gillariose"> Jiju Gillariose</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper is aimed at developing a sampling plan that uses information from precedent and successive lots for lot disposition with a pretention that the life-time of a particular product assumes a Log-logistic distribution. A Two-pronged Truncated Deferred Sampling Plan (TTDSP) for Log-logistic distribution is proposed when the testing is truncated at a precise time. The best possible sample sizes are obtained under a given Maximum Allowable Percent Defective (MAPD), Test Suspension Ratios (TSR), and acceptance numbers (c). A formula for calculating the operating characteristics of the proposed plan is also developed. The operating characteristics and mean-ratio values were used to measure the performance of the plan. The findings of the study show that: Log-logistic distribution has a decreasing failure rate; furthermore, as mean-life ratio increase, the failure rate reduces; the sample size increase as the acceptance number, test suspension ratios and maximum allowable percent defective increases. The study concludes that the minimum sample sizes were smaller, which makes the plan a more economical plan to adopt when cost and time of production are costly and the experiment being destructive. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=consumers%20risk" title="consumers risk">consumers risk</a>, <a href="https://publications.waset.org/abstracts/search?q=mean%20life" title=" mean life"> mean life</a>, <a href="https://publications.waset.org/abstracts/search?q=minimum%20sample%20size" title=" minimum sample size"> minimum sample size</a>, <a href="https://publications.waset.org/abstracts/search?q=operating%20characteristics" title=" operating characteristics"> operating characteristics</a>, <a href="https://publications.waset.org/abstracts/search?q=producers%20risk" title=" producers risk"> producers risk</a> </p> <a href="https://publications.waset.org/abstracts/127830/a-two-pronged-truncated-deferred-sampling-plan-for-log-logistic-distribution" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/127830.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">140</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=type%20II%20generalized%20logistic%20distribution&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=type%20II%20generalized%20logistic%20distribution&page=3">3</a></li> <li class="page-item"><a class="page-link" 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