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In statistics, maximum likelihood estimation (MLE) is a method of estimating the parameters of an assumed probability distribution, given some observed data. This is achieved by maximizing a likelihood function so that, under the assumed statistical model , the observed data is most probable.
Optimization tools for maximum likelihood, GMM, or maximum simulated likelihood estimators [1] Post estimation tools for simulation , hypothesis testing, and partial effects [ 1 ] Computational methods that match the National Institute of Standards and Technology test problems [ 4 ] [ 5 ]
Although an EM iteration does increase the observed data (i.e., marginal) likelihood function, no guarantee exists that the sequence converges to a maximum likelihood estimator. For multimodal distributions , this means that an EM algorithm may converge to a local maximum of the observed data likelihood function, depending on starting values.
In statistics a quasi-maximum likelihood estimate (QMLE), also known as a pseudo-likelihood estimate or a composite likelihood estimate, is an estimate of a parameter θ in a statistical model that is formed by maximizing a function that is related to the logarithm of the likelihood function, but in discussing the consistency and (asymptotic) variance-covariance matrix, we assume some parts of ...
A Comparison of Hierarchical Bayes and Maximum Simulated Likelihood for Mixed Logit Customer-Specific Taste Parameters and Mixed Logit, with David Revelt On the Similarity of Classical and Bayesian Estimates of Individual Mean Partworths, with Joel Huber, Marketing Letters, Vol. 12, No. 3, pp. 259–269, August 2001.
When the parameters are estimated using the log-likelihood for the maximum likelihood estimation, each data point is used by being added to the total log-likelihood. As the data can be viewed as an evidence that support the estimated parameters, this process can be interpreted as "support from independent evidence adds", and the log-likelihood ...
Randomized Axelerated Maximum Likelihood for High Performance Computing (nucleotides and aminoacids) Next Generation: Maximum likelihood, simple Maximum parsimony: A. Kozlov, D. Darriba, T. Flouri, B. Morel, A. Stamatakis SEMPHY Tree reconstruction using the combined strengths of maximum-likelihood (accuracy) and neighbor-joining (speed).
Pages in category "Maximum likelihood estimation" The following 10 pages are in this category, out of 10 total. ... Partial likelihood methods for panel data; Q.