The Annals of Mathematical Statistics

Optimum Estimators for Linear Functions of Location and Scale Parameters

Nancy R. Mann

Full-text: Open access

Abstract

In this paper, loss is taken to be proportional to squared error with the constant of proportionality equal to the square of the inverse of a scale parameter, and an invariant estimator is defined to be one with risk invariant under transformations of location and scale. For certain classes of estimators, best (minimum-mean-squared-error) invariant estimators are found for specified linear functions of an unknown scale parameter and one or more unknown location parameters. Even when the specified function is equal to a single location parameter, the best invariant estimator is not equal to the best unbiased estimator in the class except for complete samples from certain distributions such as the Gaussian.

Article information

Source
Ann. Math. Statist., Volume 40, Number 6 (1969), 2149-2155.

Dates
First available in Project Euclid: 27 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aoms/1177697292

Digital Object Identifier
doi:10.1214/aoms/1177697292

Mathematical Reviews number (MathSciNet)
MR260091

Zentralblatt MATH identifier
0188.50303

JSTOR
links.jstor.org

Citation

Mann, Nancy R. Optimum Estimators for Linear Functions of Location and Scale Parameters. Ann. Math. Statist. 40 (1969), no. 6, 2149--2155. doi:10.1214/aoms/1177697292. https://projecteuclid.org/euclid.aoms/1177697292


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