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Download e-book for kindle: Contemporary Developments in Statistical Theory: A by Soumendra Lahiri, Anton Schick, Ashis SenGupta, T.N. Sriram

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By Soumendra Lahiri, Anton Schick, Ashis SenGupta, T.N. Sriram

ISBN-10: 331902650X

ISBN-13: 9783319026503

ISBN-10: 3319026518

ISBN-13: 9783319026510

This quantity highlights Prof. Hira Koul’s achievements in lots of parts of data, together with Asymptotic concept of statistical inference, Robustness, Weighted empirical procedures and their functions, Survival research, Nonlinear time sequence and Econometrics, between others. Chapters are all unique papers that discover the frontiers of those parts and should support researchers and graduate scholars operating in records, Econometrics and similar parts. Prof. Hira Koul was once the 1st Ph.D. scholar of Prof. Peter Bickel. His exclusive occupation in data contains the receipt of many prestigious awards, together with the Senior Humbolt award (1995), and committed carrier to the career via editorial paintings for journals and during management roles in expert societies, significantly because the earlier president of the overseas Indian Statistical organization. Prof. Hira Koul has graduated on the subject of 30 Ph.D. scholars, and made numerous seminal contributions in approximately a hundred twenty five cutting edge learn papers. The lengthy checklist of his exceptional collaborators is represented via the individuals to this volume.

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Extra info for Contemporary Developments in Statistical Theory: A Festschrift for Hira Lal Koul

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5). b) We precisely quantify the risk at √kn for fixed positive k (Eq. 5). 2). d) We show that the global peak of the risk is not attained within n−1/2 neighborhoods. 5, Eq. 26)), and that immediately below θ = cn , the risk is even higher. Precisely, we exhibit explicit and parsimonious shrinking neighborhoods Un of θ = cn , such that lim inf cn−2 sup R(θ, Sn ) ≥ 1. 6, Eq. 28)). 4) with an lim inf, rather than lim sup. Specifically, our calculations indicate that argmaxθ R(θ , Sn ) ≈ cn − log (ncn2 ) , n and supθ R(θ , Sn ) ≈ cn2 − 2cn logn n (Eq.

4 involve use of standard facts about the standard normal tail and rearrangement of terms. We omit these calculations. It follows from part (b) of this theorem, by letting k → ∞ that for the original Hodges’ estimate Tn , supθ R(θ, Tn ) >> n1 for large n, in the following sense. 4 Asymptotic Risk and Bayes Risk of Thresholding and Superefficient . . 51 15 10 5 1 2 3 4 k Fig. 4 If an → 0 and ncn → ∞, then limn supθ nR(θ , Sn ) = ∞. On the other hand, part (c) and part (d) of the above theorem together lead to the following asymptotic expansions for the risk of Hodges’original estimate Tn at θ = 0 and θ = √1n .

For the second part of the theorem, choose a subsequence {ank } of {an } converging to a. For notational brevity, we denote the subsequence as an itself. 20) applies. Putting together 50 A. DasGupta and I. M. 21), and the Bolzano-Weierstrass theorem, we have one subsequence for which the limit of nR(θn , Sn ) is ≥ a 2 +1, and hence, lim supn nR(θn , Sn ) ≥ a 2 +1. We will now see that if we strengthen √ our control on the sequence {an } to require it to have a limit, and likewise require ncn also to have a limit, then the (normalized) risk of Sn at √kn will also have a limit for any given k.

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Contemporary Developments in Statistical Theory: A Festschrift for Hira Lal Koul by Soumendra Lahiri, Anton Schick, Ashis SenGupta, T.N. Sriram


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