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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 ]
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.
In statistics, an expectation–maximization (EM) algorithm is an iterative method to find (local) maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables. [1]
But for practical purposes it is more convenient to work with the log-likelihood function in maximum likelihood estimation, in particular since most common probability distributions—notably the exponential family—are only logarithmically concave, [34] [35] and concavity of the objective function plays a key role in the maximization.
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 ...
Due to easy computability, method-of-moments estimates may be used as the first approximation to the solutions of the likelihood equations, and successive improved approximations may then be found by the Newton–Raphson method. In this way the method of moments can assist in finding maximum likelihood estimates.
Pages in category "Maximum likelihood estimation" The following 10 pages are in this category, out of 10 total. This list may not reflect recent changes. ...