The Annals of Mathematical Statistics

Efficient Estimation of a Shift Parameter From Grouped Data

P. K. Bhattacharya

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Abstract

Universally efficient procedures for testing and estimation problems have been briefly explored by Hajek [3] and Stein [7]. In this paper we consider two populations having frequency functions $f(x)$ and $f(x - \theta)$ where the common form $f$ and the shift parameter $\theta$ are unknown. A method of estimating $\theta$ when one sample is reduced to a frequency distribution over a given set of class-intervals is suggested by the likelihood principle and the asymptotic efficiency of this estimator relative to the appropriate maximum likelihood estimator based on the complete data is found to be the ratio of the Fisher-information in a grouped observation to the Fisher-information in an ungrouped observation.

Article information

Source
Ann. Math. Statist., Volume 38, Number 6 (1967), 1770-1787.

Dates
First available in Project Euclid: 27 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aoms/1177698611

Digital Object Identifier
doi:10.1214/aoms/1177698611

Mathematical Reviews number (MathSciNet)
MR220415

Zentralblatt MATH identifier
0155.26002

JSTOR
links.jstor.org

Citation

Bhattacharya, P. K. Efficient Estimation of a Shift Parameter From Grouped Data. Ann. Math. Statist. 38 (1967), no. 6, 1770--1787. doi:10.1214/aoms/1177698611. https://projecteuclid.org/euclid.aoms/1177698611


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