On the invariant estimation of an exponential scale using doubly censored data

Mohamed T. Madi

doi:10.1016/S0167-7152(01)00174-2

On the invariant estimation of an exponential scale using doubly censored data

Mohamed T. Madi

Research output: Contribution to journal › Article › peer-review

10 Citations (Scopus)

Abstract

We consider the problem of estimating the scale parameter χ of the shifted exponential distribution with unknown shift based on a doubly censored sample from this distribution. Under squared error loss, Elfessi (Statist. Probab. Lett. 36 (1997) 251) has shown that the best affine equivariant estimator (BAEE) of χ is inadmissible. A smoother dominating procedure is proposed. The new improved estimator is shown, via a numerical study, to provide more significant risk reductions over the BAEE.

Original language	English
Pages (from-to)	77-82
Number of pages	6
Journal	Statistics and Probability Letters
Volume	56
Issue number	1
DOIs	https://doi.org/10.1016/S0167-7152(01)00174-2
Publication status	Published - Jan 1 2002

Keywords

Doubly censored
Entropy loss
Equivariant estimator
Exponential distribution
Risk reduction
Scale parameter
Squared error loss

ASJC Scopus subject areas

Statistics and Probability
Statistics, Probability and Uncertainty

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10.1016/S0167-7152(01)00174-2

Cite this

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AB - We consider the problem of estimating the scale parameter χ of the shifted exponential distribution with unknown shift based on a doubly censored sample from this distribution. Under squared error loss, Elfessi (Statist. Probab. Lett. 36 (1997) 251) has shown that the best affine equivariant estimator (BAEE) of χ is inadmissible. A smoother dominating procedure is proposed. The new improved estimator is shown, via a numerical study, to provide more significant risk reductions over the BAEE.

KW - Doubly censored

KW - Entropy loss

KW - Equivariant estimator

KW - Exponential distribution

KW - Risk reduction

KW - Scale parameter

KW - Squared error loss

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On the invariant estimation of an exponential scale using doubly censored data

Abstract

Keywords

ASJC Scopus subject areas

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