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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On the asymptotic distributional risk properties of pre-test and shrinkage L 1 -estimators
TL;DR: In this article, both preliminary test and shrinkage L1-estimators based on the usual L1 estimators are considered and the relative asymptotic risk-efficiency results are studied in detail.
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Effect of stroke prevention medication on aorticatheroma progression assessed using new statisticalparadigm
TL;DR: Progression of aortic arch atheroma (AA) is associated with vascular events in patients with stroke or transient ischemic attack (TIA) and studies investigating effect of stroke prevention find it to be associated with stroke prevention.
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Asymptotic relative efficiency of multivariate m-estimators
Julio M. Singer,Pranab Kumar Sen +1 more
TL;DR: In this paper, the authors compared the robust coordinatewise M-estimators with the robust Maronna-type M estimators proposed by Singer and Sen (1985) under different elliptically symmetric error distributions.
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The Cox regression model, random censoring and locally optimal rank tests
TL;DR: Conditions on the hazard functions under the usual log-rank test remains locally optimal for the Cox regression model under random censoring (withdrawal) and the asymptotic efficiency results pertaining to the Cox partial likelihood statistic and the log- rank statistic are studied.
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The extended two-sample problem: nonparametric case
TL;DR: In this paper, the classical two-sample problem is extended to the case where the distribution functions of the observable random variables are specified functions of unknown distribution functions and the null hypotheses to be tested or the parameters to be estimated relate to these unknown distributions.