Maximum Likelihood Estimation
Maximum Likelihood Estimation (MLE) is a general-purpose parametric method for estimating the unknown parameters of a statistical model by finding the parameter values that make the observed data most probable. Formalized by R. A. Fisher in his landmark 1922 paper in the Philosophical Transactions of the Royal Society, MLE has become the dominant parameter-estimation paradigm in modern statistics and is the foundational engine behind logistic regression, generalized linear models, structural equation modeling, and virtually all parametric inference procedures.
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- Fisher, R. A. (1922). On the mathematical foundations of theoretical statistics. Philosophical Transactions of the Royal Society of London, Series A, 222, 309–368. · DOI 10.1098/rsta.1922.0009
- Casella, G., & Berger, R. L. (2002). Statistical Inference (2nd ed.). Duxbury Press / Cengage Learning. · ISBN 978-0534243128
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