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1961•Journal of the Royal Statistical Society Series B (Statistical Methodology)Requires access

A Method of Maximum-Likelihood Estimation

Frank Richards

Open publisher page 207 citations

Abstract

SUMMARY Knowledge of one or more of the parameters in a maximum-likelihood problem sometimes results in a very considerable simplification, the analysis becoming almost trivial. A method of obtaining maximum-likelihood estimates and their asymptotic covariance matrix is given which makes it possible to capitalize on simplifications of this sort when they arise. Application is mainly in the field of regression, but other examples are mentioned. A numerical example of exponential regression is given.

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SUMMARY Knowledge of one or more of the parameters in a maximum-likelihood problem sometimes results in a very considerable simplification, the analysis becoming almost trivial. A method of obtaining maximum-likelihood estimates and their asymptotic covariance matrix is given which makes it possible to capitalize on simplifications of this sort when they arise. Application is mainly in the field of regression, but other examples are mentioned. A numerical example of exponential regression is given.

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Available abstract

SUMMARY Knowledge of one or more of the parameters in a maximum-likelihood problem sometimes results in a very considerable simplification, the analysis becoming almost trivial. A method of obtaining maximum-likelihood estimates and their asymptotic covariance matrix is given which makes it possible to capitalize on simplifications of this sort when they arise. Application is mainly in the field of regression, but other examples are mentioned. A numerical example of exponential regression is given.

Key concepts: Maximum likelihood, sort, Maximum likelihood sequence estimation, Mathematics, Restricted maximum likelihood, Applied mathematics, Covariance matrix, Statistics

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