The Annals of Mathematical Statistics

Asymptotic Properties of the Maximum Likelihood Estimate of an Unknown Parameter of a Discrete Stochastic Process

Abraham Wald

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Abstract

Asymptotic properties of maximum likelihood estimates have been studied so far mainly in the case of independent observations. In this paper the case of stochastically dependent observations is considered. It is shown that under certain restrictions on the joint probability distribution of the observations the maximum likelihood equation has at least one root which is a consistent estimate of the parameter $\theta$ to be estimated. Furthermore, any root of the maximum likelihood equation which is a consistent estimate of $\theta$ is shown to be asymptotically efficient. Since the maximum likelihood estimate is always a root of the maximum likelihood equation, consistency of the maximum likelihood estimate implies its asymptotic efficiency.

Article information

Source
Ann. Math. Statist., Volume 19, Number 1 (1948), 40-46.

Dates
First available in Project Euclid: 28 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aoms/1177730288

Digital Object Identifier
doi:10.1214/aoms/1177730288

Mathematical Reviews number (MathSciNet)
MR24114

Zentralblatt MATH identifier
0032.17301

JSTOR
links.jstor.org

Citation

Wald, Abraham. Asymptotic Properties of the Maximum Likelihood Estimate of an Unknown Parameter of a Discrete Stochastic Process. Ann. Math. Statist. 19 (1948), no. 1, 40--46. doi:10.1214/aoms/1177730288. https://projecteuclid.org/euclid.aoms/1177730288


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