P
Pranab Kumar Sen
Researcher at University of North Carolina at Chapel Hill
Publications - 572
Citations - 23008
Pranab Kumar Sen is an academic researcher from University of North Carolina at Chapel Hill. The author has contributed to research in topics: Estimator & Nonparametric statistics. The author has an hindex of 51, co-authored 570 publications receiving 19997 citations. Previous affiliations of Pranab Kumar Sen include Indian Statistical Institute & Academia Sinica.
Papers
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Book ChapterDOI
Bivariate Survival Data Under Censoring: Simulation Procedure for Group Sequential Boundaries
TL;DR: In this paper, the authors consider the bivariate exponential distribution of Sarkar to develop a parametric methodology for interim analysis of clinical trials and present the procedure for testing the hypothesis of no treatment difference assuming complete uncensored data.
Journal ArticleDOI
Nonparametric test of restricted interchangeability
TL;DR: In this article, the rank order test under the null hypothesis of restricted interchangeability was proposed and the asymptotic relative efficiency of this nonparametric test in comparison with Votaw's (1948, Ann. Math. Statist, 19, 447,473) likelihood ratio test was given.
Journal ArticleDOI
A partial likelihood-based two-dimensional multistate markov model with application to myocardial infarction and stroke recurrence
TL;DR: A partial likelihood approach was adopted by using the Sarkar's absolutely continuous bivariate exponential distribution (ACBVE) separately for the transitions among different states and showed that the parametric ACBVE explained the data well.
Book ChapterDOI
Introduction: Wither Bioinformatics in Human Health and Heredity
TL;DR: This chapter briefly outlines the premises with which integration of information from several disciplines are used in this newly introduced discipline, and lists some of the open areas of research in bioinformatics.
Journal ArticleDOI
Nonparametric estimation of the generalized variance
Bimal K. Sinha,Pranab Kumar Sen +1 more
TL;DR: In this article, a nonparametric symmetric unbiased estimator of the generalized variance is considered, and it is shown to be (nonparametric) optimal for the class of distributions having finite fourth order moments.