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.
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Journal ArticleDOI
On Stochastic Ordering and a General Class of Poverty Indexes
TL;DR: In this article, a modification is proposed to develop certain stochastic ordering monotonicity preserving diversity measures that overcome this drawback, and such measures are shown to be invariant under increasing transformation, and thereby appropriate for partially ordered categorical response data models.
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Resampling method to estimate intra-cluster correlation for clustered binary data
TL;DR: It is concluded from the simulation study that the resampling-based estimates approximate the population ICC more precisely than the analysis of variance and method of moments techniques for different event rates, varying number of clusters, and cluster sizes.
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Asymptotic normality of the extremum of certain sample functions
Book ChapterDOI
Introduction to Rao (1948) Large Sample Tests of Statistical Hypotheses Concerning Several Parameters with Applications to Problems of Estimation
TL;DR: In this article, C.R. Rao has emerged as one of the most outstanding mathematical statisticians of our time, and his fundamental research contributions covering a wider spectrum of mathematical statistics, design of experiment, combinatorial mathematics, multivariate analysis, information theory, sample surveys, biometry, econometrics, and a variety of other fields, span over a period of little more than 50 years.
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On Weak Convergence of Empirical Processes for Random Number of Independent Stochastic Vectors
TL;DR: In this article, the results of Pyke on weak convergence of the empirical process with random sample size are simplified and extended to the case of p(>1)-dimensional stochastic vectors.