Maximum Likelihood Estimation
Maximum Likelihood Estimation is a statistical inference method that estimates model parameters by maximizing the likelihood function, distinguishing it from other estimation techniques through its foundation on the probability of observing the given data under a specified statistical model.
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2M
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2017–2026
3.1K
Variants
Mle
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Fitting Linear Mixed-Effects Models Using <b>lme4</b>
Douglas M. Bates, Martin Mächler, Benjamin M. Bolker et al. · Journal of Statistical Software · 2015 · 85.6K citations · Full text
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Stanford University
Stanford, United States
86 h-index
405 publications
126.2K citations
University of California, Berkeley
Berkeley, United States
83 h-index
397 publications
65.8K citations
University of Chicago
Chicago, United States
71 h-index
251 publications
42K citations
Massachusetts Institute of Technology
Cambridge, United States
67 h-index
281 publications
29.3K citations
University of Michigan
Ann Arbor, United States
64 h-index
335 publications
33.8K citations
Princeton University
Princeton, United States
64 h-index
240 publications
28.4K citations
University of Wisconsin–Madison
Madison, United States
64 h-index
267 publications
115.6K citations
University of California, Los Angeles
Los Angeles, United States
62 h-index
243 publications
36.3K citations
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944 publications · 170.4K citations
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696 publications · 99.8K citations
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562 publications · 69K citations
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276 publications · 170.3K citations
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225 publications · 97.8K citations
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