Optimal equivariant estimator with respect to convex loss function

DOI10.1016/S0378-3758(97)00036-0zbMath0914.62044OpenAlexW1971370493MaRDI QIDQ1378818

S. Kalpana Bai, T. M. Durairajan

Publication date: 14 June 1999

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0378-3758(97)00036-0

zbMATH Keywords

characterizationequivariant estimatorlocationscalelocation-scale familiesconvex loss functions

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Point estimation (62F10) Foundations and philosophical topics in statistics (62A01) Parametric inference (62F99)

Related Items (2)

Minimum Convex Risk Equivariant Finite Population Prediction for Linear Functions in Regression Models ⋮ Stochastic comparisons of stratified sampling techniques for some Monte Carlo estimators

Cites Work

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• Equivariant estimation of a mean vector \(\mu\) of N(\(\mu\) ,\(\Sigma\) ) with \(\mu '\Sigma ^{-1}\mu =1\) or \(\Sigma ^{-}\mu =c\) or \(\Sigma =\sigma\) 2\(\mu\) '\(\mu\) I
• Invariance, Minimax Sequential Estimation, and Continuous Time Processes
• A necessary and sufficient condition for an estimator to be optimal
• Minimum Risk Equivariant Estimators of Percentiles in LocationScale Families of Distributions
• Equivariant estimation of a normal mean vector using a normal concomitant vector for covariance adjustment
• THE ESTIMATION OF THE LOCATION AND SCALE PARAMETERS OF A CONTINUOUS POPULATION OF ANY GIVEN FORM

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