Conditional robustness in location estimation

Thomas A. Severini

doi:10.1093/biomet/79.1.69

Conditional robustness in location estimation

Thomas A. Severini^*

^*Corresponding author for this work

Statistics and Data Science

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

1 Scopus citations

Abstract

SUMMARY: Let Y₁,..., Y_n denote independent real-valued observations, each distributed according to a density p(y - θ ), where θ is an unknown parameter and p is a symmetric density function. This paper considers robust point estimation of θ from the point of view of conditional inference. Specific measures of the conditional robustness of a location estimator are introduced, as well as measures of the conditional robustness of a particular sample. Location-scale models are also considered. The results are applied to several data sets.

Original language	English (US)
Pages (from-to)	69-79
Number of pages	11
Journal	Biometrika
Volume	79
Issue number	1
DOIs	https://doi.org/10.1093/biomet/79.1.69
State	Published - Mar 1992

Keywords

Conditional inference
Inference robustness
L-estimates
Location-scale models
Robust estimation

ASJC Scopus subject areas

Statistics and Probability
General Mathematics
Agricultural and Biological Sciences (miscellaneous)
General Agricultural and Biological Sciences
Statistics, Probability and Uncertainty
Applied Mathematics

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10.1093/biomet/79.1.69

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N2 - SUMMARY: Let Y1,..., Yn denote independent real-valued observations, each distributed according to a density p(y - θ ), where θ is an unknown parameter and p is a symmetric density function. This paper considers robust point estimation of θ from the point of view of conditional inference. Specific measures of the conditional robustness of a location estimator are introduced, as well as measures of the conditional robustness of a particular sample. Location-scale models are also considered. The results are applied to several data sets.

AB - SUMMARY: Let Y1,..., Yn denote independent real-valued observations, each distributed according to a density p(y - θ ), where θ is an unknown parameter and p is a symmetric density function. This paper considers robust point estimation of θ from the point of view of conditional inference. Specific measures of the conditional robustness of a location estimator are introduced, as well as measures of the conditional robustness of a particular sample. Location-scale models are also considered. The results are applied to several data sets.

KW - Conditional inference

KW - Inference robustness

KW - L-estimates

KW - Location-scale models

KW - Robust estimation

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Conditional robustness in location estimation

Abstract

Keywords

ASJC Scopus subject areas

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