The Experts below are selected from a list of 318 Experts worldwide ranked by ideXlab platform
Narayanaswamy Balakrishnan - One of the best experts on this subject based on the ideXlab platform.
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A General Purpose Approximate Goodness-of-Fit Test for Progressively Type-II Censored Data
IEEE Transactions on Reliability, 2012Co-Authors: Reza Pakyari, Narayanaswamy BalakrishnanAbstract:We propose a general purpose approximate goodness-of-fit test that covers several families of distributions under progressive Type-II Censored Data. The test procedure is based on the empirical distribution function (EDF), and generalizes the goodness-of-fit test proposed by Chen and Balakrishnan [11] to progressively Type-II Censored Data. The new method requires some tables for critical values, which are constructed by Monte Carlo simulation. The power of the proposed tests are then assessed for several alternative distributions, while testing for normal, Gumbel, and log-normal distributions, through Monte Carlo simulations. It is observed that the proposed tests are quite powerful when compared to an existing goodness-of-fit test proposed for progressively Type-II Censored Data due to Balakrishnan et al. . The proposed goodness-of-fit test is then illustrated with two real Data sets.
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Testing Exponentiality Based on Kullback-Leibler Information With Progressively Type-II Censored Data
IEEE Transactions on Reliability, 2007Co-Authors: Narayanaswamy Balakrishnan, Naser Reza ArghamiAbstract:We express the joint entropy of progressively Censored order statistics in terms of an incomplete integral of the hazard function, and provide a simple estimate of the joint entropy of progressively Type-II Censored Data. We then construct a goodness-of-fit test statistic based on Kullback-Leibler information with progressively Type-II Censored Data. Finally, by using Monte Carlo simulations, the power of the test is estimated, and compared against several alternatives under different progressive censoring schemes
Sangun Park - One of the best experts on this subject based on the ideXlab platform.
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On the Kullback–Leibler information of hybrid Censored Data
Communications in Statistics-theory and Methods, 2015Co-Authors: Sangun ParkAbstract:ABSTRACTA hybrid censoring is a mixture of Type I and Type II censoring where the experiment terminates when either rth failure or predetermined censoring time comes first or later. In this article, we consider order statistics of the Type I Censored Data and provide a simple expression for their Kullback–Leibler (KL) information. Then, we provide the expressions for the KL information of the Type I and Type II hybrid Censored Data.
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Testing exponentiality based on the Kullback-Leibler information with the type II Censored Data
IEEE Transactions on Reliability, 2005Co-Authors: Sangun ParkAbstract:We express the joint entropy of order statistics in terms of an incomplete integral of the hazard function, and provide a simple estimate of the joint entropy of the type II Censored Data. Then we establish a goodness of fit test statistic based on the Kullback-Leibler information with the type II Censored Data, and compare its performance with some leading test statistics. A Monte Carlo simulation study shows that the proposed test statistic shows better powers than some leading test statistics against the alternatives with monotone increasing hazard functions.
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On the Fisher Information in Multiply Censored and Progressively Censored Data
Communications in Statistics-theory and Methods, 2004Co-Authors: Gang Zheng, Sangun ParkAbstract:In this article, we first show that the asymptotic Fisher information contained in multiple interval Censored and multiple Type II Censored Data are asymptotically equivalent. We then extend the results to randomly Censored Data. Finally, we study the calculation of the exact Fisher information contained in Type II progressively Censored Data. In this case, the Fisher information can be written as a sum of several single integrations.
Naser Reza Arghami - One of the best experts on this subject based on the ideXlab platform.
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Testing Exponentiality Based on Kullback-Leibler Information With Progressively Type-II Censored Data
IEEE Transactions on Reliability, 2007Co-Authors: Narayanaswamy Balakrishnan, Naser Reza ArghamiAbstract:We express the joint entropy of progressively Censored order statistics in terms of an incomplete integral of the hazard function, and provide a simple estimate of the joint entropy of progressively Type-II Censored Data. We then construct a goodness-of-fit test statistic based on Kullback-Leibler information with progressively Type-II Censored Data. Finally, by using Monte Carlo simulations, the power of the test is estimated, and compared against several alternatives under different progressive censoring schemes
Clifford Andersonbergman - One of the best experts on this subject based on the ideXlab platform.
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icenreg regression models for interval Censored Data in r
Journal of Statistical Software, 2017Co-Authors: Clifford AndersonbergmanAbstract:The non-parametric maximum likelihood estimator and semi-parametric regression models are fundamental estimators for interval Censored Data, along with standard fullyparametric regression models. The R package icenReg is introduced which contains fast, reliable algorithms for fitting these models. In addition, the package contains functions for imputation of the Censored response variables and diagnostics of both regression effects and baseline distribution.
Rebecca A Betensky - One of the best experts on this subject based on the ideXlab platform.
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multiple imputation for simple estimation of the hazard function based on interval Censored Data
Statistics in Medicine, 2000Co-Authors: Judith D Bebchuk, Rebecca A BetenskyAbstract:: A Data augmentation algorithm is presented for estimating the hazard function and pointwise variability intervals based on interval Censored Data. The algorithm extends that proposed by Tanner and Wong for grouped right Censored Data to interval Censored Data. It applies multiple imputation and local likelihood methods to obtain smooth non-parametric estimates for the hazard function. This approach considerably simplifies the problem of estimation for interval Censored Data as it transforms it into the more tractable problem of estimation for right Censored Data. The method is illustrated for two real Data sets: times to breast cosmesis deterioration and times to HIV-1 infection for individuals with haemophilia. Simulations are presented to assess the effects of various parameters on the estimates and their variances.
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Redistribution algorithms for Censored Data
Statistics & Probability Letters, 2000Co-Authors: Rebecca A BetenskyAbstract:Efron (Proceedings of the Fifth Berkeley Symposium, Vol. 4, University of California Press, Berkeley, CA, pp. 831-853) and Dinse (Amer. Statist. 39, 1985, 299-300) proposed redistribution of mass algorithms for survivor function estimation from right Censored Data. Dinse's algorithm is easily extended to survivor function estimation from interval Censored Data and is further extended to incorporate information on disease markers.
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a non parametric maximum likelihood estimator for bivariate interval Censored Data
Statistics in Medicine, 1999Co-Authors: Rebecca A Betensky, Dianne M FinkelsteinAbstract:: We derive a non-parametric maximum likelihood estimator for bivariate interval Censored Data using standard techniques for constrained convex optimization. Our approach extends those taken for univariate interval Censored Data. We illustrate the estimator with bivariate Data from an AIDS study.