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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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Taksonoomiline meetodikirje · regression-model / statistics

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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Used in the same domainEM Algorithmmachine-suggested · Relational suggestion, not evidence.See alsoLogistic Regressionmachine-suggested · Relational suggestion, not evidence.See alsoMethod of Momentsmachine-suggested · Relational suggestion, not evidence.See alsoStructural Equation Modelingmachine-suggested · Relational suggestion, not evidence.