Accession Number : AD0712032

Title :   ASYMPTOTIC MINIMAX AND ADMISSIBILITY IN ESTIMATION.

Descriptive Note : Technical rept.,

Corporate Author : FLORIDA STATE UNIV TALLAHASSEE DEPT OF STATISTICS

Personal Author(s) : Hajek,Jaroslav

Report Date : AUG 1970

Pagination or Media Count : 38

Abstract : A sequence of general experiments is considered over a k-dimensional parameter. Under conditions of local asymptotic normality (LAN) of the families of distributions, we prove that, from the point of view of the local asymptotic minimax, there is a lower bound, which may be obtained only if the estimator has certain linear relation to the derivative of the likelihood function. This entails asymptotic normality with Fisher's variance. Conditions LAN are proved under the sole condition of continuity of Fisher's information. (Author)

Descriptors :   (*PROBABILITY, STATISTICAL TESTS), DISTRIBUTION THEORY, MINIMAX TECHNIQUE, ASYMPTOTIC SERIES, MATRICES(MATHEMATICS), THEOREMS

Subject Categories : Statistics and Probability

Distribution Statement : APPROVED FOR PUBLIC RELEASE