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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.
[1] [2] [3] When evaluated on the actual data points, it becomes a function solely of the model parameters. In maximum likelihood estimation, the argument that maximizes the likelihood function serves as a point estimate for the unknown parameter, while the Fisher information (often approximated by the likelihood's Hessian matrix at the maximum ...
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]
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 ...
Rasch model estimation; Restricted maximum likelihood; S. Scoring algorithm; T. Testing in binary response index models
In statistics, the restricted (or residual, or reduced) maximum likelihood (REML) approach is a particular form of maximum likelihood estimation that does not base estimates on a maximum likelihood fit of all the information, but instead uses a likelihood function calculated from a transformed set of data, so that nuisance parameters have no effect.