http://www.i-scholar.in/index.php/Adst/issue/feedAdvances in Statistics2016-04-28T05:01:24+00:00Dr. Mohammmad Fraiwan Al-Salehas@hindawi.comOpen Journal Systems<p>Advances in Statistics is a peer-reviewed, open access journal that publishes original research articles as well as review articles in all areas of statistics.</p><p> </p>http://www.i-scholar.in/index.php/Adst/article/view/98193Estimation in Step-Stress Accelerated Life Tests for Power Generalized Weibull Distribution with Progressive Censoring2016-04-28T05:01:19+00:00M. M. Mohie EL-DinS. E. Abu-YoussefNahed S. A. AliA. M. Abd El-Raheema m2am@yahoo.comBased on progressive censoring, step-stress partially accelerated life tests are considered when the lifetime of a product follows power generalized Weibull distribution. The maximum likelihood estimates (MLEs) and Bayes estimates (BEs) are obtained for the distribution parameters and the acceleration factor. In addition, the approximate and bootstrap confidence intervals (CIs) of the estimators are presented. Furthermore, the optimal stress change time for the step-stress partially accelerated life test is determined by minimizing the asymptotic variance of MLEs of themodel parameters and the acceleration factor. Simulation results are carried out to study the precision of the MLEs and BEs for the parameters involved.http://www.i-scholar.in/index.php/Adst/article/view/98194Statistical Inference in Dependent Component Hybrid Systems with Masked Data2016-04-28T05:01:23+00:00Naijun Shansha@utep.eduRonghua WangPing HuXiaoling XuComplex systems are usually composed of simple hybrid systems. In this paper,we consider statistical inference for two fundamental hybrid systems: series-parallel and parallel-series systems based on masked data. Assuming dependent lifetimes of components modelled by Marshall and Olkin's bivariate exponential distribution in the system, we present maximum likelihood and interval estimation of parameters of interest. Intensive simulation studies are performed to demonstrate the efficiency of the methods.http://www.i-scholar.in/index.php/Adst/article/view/98196Relative Entropies and Jensen Divergences in the Classical Limit2016-04-28T05:01:24+00:00A. M. Kowalskikowalski@fisica.unlp.edu.arA. PlastinoMetrics and distances in probability spaces have shown to be useful tools for physical purposes. Here we use this idea, with emphasis on Jensen Divergences and relative entropies, to investigate features of the road towards the classical limit. A well-known semiclassical model is used and recourse is made to numerical techniques, via the well-known Bandt and Pompe methodology, to extract probability distributions from the pertinent time-series associated with dynamical data.http://www.i-scholar.in/index.php/Adst/article/view/98198Estimation of the Derivatives of a Function in a Convolution Regression Model with Random Design2016-04-28T05:01:24+00:00Christophe Chesneauchristophe.chesneau@gmail.comMaher KachourA convolution regression model with random design is considered. We investigate the estimation of the derivatives of an unknown function, element of the convolution product. We introduce new estimators based on wavelet methods and provide theoretical guarantees on their good performances.http://www.i-scholar.in/index.php/Adst/article/view/98200Bayesian Estimation of Inequality and Poverty Indices in Case of Pareto Distribution Using Different Priors under LINEX Loss Function2016-04-28T05:01:24+00:00Kamaljit Kaurkamaljitk010@gmail.comSangeeta AroraKalpana K. MahajanBayesian estimators of Gini index and a Poverty measure are obtained in case of Pareto distribution under censored and complete setup. The said estimators are obtained using two noninformative priors, namely, uniform prior and Jeffreys' prior, and one conjugate prior under the assumption of Linear Exponential (LINEX) loss function. Using simulation techniques, the relative efficiency of proposed estimators using different priors and loss functions is obtained. The performances of the proposed estimators have been compared on the basis of their simulated risks obtained under LINEX loss function.