Date of this Version
Annals of Statistics
This paper contains a complete class theorem (Theorem 3.2) which applies to most statistical estimation problems having a finite sample space. This theorem also applies to many other statistical problems with finite sample spaces. The description of this complete class involves a stepwise algorithm. At each step of the process it is necessary to construct the Bayes procedures in a suitably modified version of the original problem. The complete class is a minimal complete class if the loss function is strictly convex. Some examples are given to illustrate the application of this complete class theorem. Among these is a new result concerning the estimation of the parameters of a multinomial distribution under a normalized quadratic loss function. (See Example 4.5).
complete class theorem, finite sample space, admissible procedures, Bayes procedure, estimation, binomial distribution, multinomial distribution, strictly convex loss, squared error loss, maximum likelihood estimate
Brown, L. D. (1981). A Complete Class Theorem for Statistical Problems With Finite Sample Spaces. Annals of Statistics, 9 (6), 1289-1300. http://dx.doi.org/10.1214/aos/1176345645
Date Posted: 27 November 2017
This document has been peer reviewed.