Estimation of the support of a discrete distribution

Let Y be a positive integer-valued random variable with the probability mass function P θ(Y=y)=f(y;r)/a(θ), y=r,r+1,…,θ , where r is a known positive integer, and θ∈Θ={r,r+1,…} is an unknown parameter. We show that, for estimating θ, cY is inadmissible under both 0–1 and a general loss whenever 0<...

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Bibliographic Details
Published in:	Statistics & probability letters Vol. 48; no. 3; pp. 287 - 292
Main Authors:	Pal, Nabendu, Shen, Wei-Hsiung, Sinha, Bimal K.
Format:	Journal Article
Language:	English
Published:	Amsterdam Elsevier B.V 01.07.2000 Elsevier
Series:	Statistics & Probability Letters
Subjects:	0–1 loss Admissible Admissible Hammersley-Chapman-Robbins inequality Mean-squared error Minimax Population size Squared error loss 0-1 loss Exact sciences and technology Hammersley–Chapman–Robbins inequality Mathematics Mean-squared error Minimax Nonparametric inference Population size Probability and statistics Sciences and techniques of general use Squared error loss Statistics 62G05 Minimax Admissible Hammersley–Chapman–Robbins inequality Squared error loss Population size Mean-squared error 0–1 loss Bayes estimation Admissibility Sample size Hammersley Chapman Robins inequality Discrete distribution Minimaxity Minimax method Sampling without replacement Support estimation Mean square error
ISSN:	0167-7152, 1879-2103
Online Access:	Get full text
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