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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U-tests for variance components in linear mixed models
TL;DR: In this paper, a U-statistics-based test for null variance components in linear mixed models was proposed and obtained its asymptotic distribution (for increasing number of units) under mild regularity conditions that include only the existence of the second moment for the random effects and of the fourth moment for conditional errors.
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A random effects model for multivariate failure time data from multicenter clinical trials.
TL;DR: A random effects model for analyzing multivariate failure time data, based on large sample theory, and an estimating equation for the mean hazard ratio parameter are proposed, showing that the variability of the treatment effect is higher than found by means of simpler models.
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Inference based on ranks for the multiple-design multivariate linear model
TL;DR: In this article, an approach to statistical inference is presented under the multiple-design multivariate linear model based on multivariate rank and aligned rank statistics, which has received considerable attention in both the statistical and econometric literature; see Srivastava (1966, 1967, 1968 and Kleinbaum (1973).
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Goodness-of-fit test with nuisance regression and scale
TL;DR: In this article, the goodness-of-fit hypothesis is tested for a specified symmetric distribution F ≥ 0, not necessarily normal, in the linear model with unknown (β, σ), β∈{\open R}p, σ>0, and with i.i.d. errors e ≥ 0.
Journal Article
An extension of billingsley's uniform boundedness theorem to higher dimensional m-processes
Jana Jurečková,Pranab Kumar Sen +1 more
TL;DR: For a general class of multi-dimensional M-processes, an extension of Billingsley's uniform boundedness theorem is considered under less stringent regularity conditions, and applications of this result in statistical inference are stressed.