
Review of Short Phrases and Links 
This Review contains major "Maximum Likelihood" related terms, short phrases and links grouped together in the form of Encyclopedia article.
Definitions
 Maximum likelihood is a statisticsbased method.
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 Maximum likelihood is a widely used method for estimating model parameters.
 Maximum likelihood is used to determine the K states.
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 Maximum likelihood is the usual method used for estimating the parameters associated with various effects.
 Maximum likelihood is one of the most widely used techniques to infer evolutionary histories.
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 In addition, the nearest neighbor maximum likelihood estimate of spatial dependence could have diagnostic value.
 By default, the procedure calculates a maximum likelihood estimate for .
 PROBIT uses analytic first and second derivatives to obtain maximum likelihood estimates via the NewtonRaphson algorithm.
 Jöreskog, K.G. (1967). Some contributions to maximum likelihood factor analysis.
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 The unknown parameters  are typically estimated with maximum likelihood, quasimaximum likelihood, or Bayesian techniques.
 Data file : school.txt fiml.sha Full information maximum likelihood  Klein Model I.
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 Options to maximum likelihood procedures All maximum likelihood procedures share a common set of options that control the maximization algorithm and output.
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 You learned about maximum likelihood estimators, binomial random variables, Bernoulli processes, the beta distribution, and conjugate priors.
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 Asymptotics of maximum likelihood estimators, distribution functions, quantiles.
 General examples are the selection of a model indexing a maximum likelihood estimator, and the selection of a bandwidth indexing a nonparametric (e.g.
 LL is calculated through iteration, using maximum likelihood estimation (MLE). Log likelihood is the basis for tests of a logistic model.
 Maximum likelihood estimation, MLE, is the method used to calculate the logit coefficients.
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 Empirical estimators, method of moments, maximum likelihood estimation.
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 Maximum likelihood, density estimation, Markov chains, classification.
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 Nonparametric maximum likelihood estimation of an increasing hazard rate for uncertain causeofdeath data.
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 If you specify THETA=EST, a maximum likelihood estimate is computed for .
 Maximum likelihood estimates in exponential response models.
 DAS, K. 1979 . Asy mptotic optimality of restricted maximum likelihood estimates for the mixed model.
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 There are other methods for finding maximum likelihood estimates, such as gradient descent, conjugate gradient or variations of the GaussNewton method.
 Martin DO, Austin H. An efficient program for computing conditional maximum likelihood estimates and exact confidence limits for a common odds ratio.
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 An EM algorithm can also find maximum a posteriori (MAP) estimates, by performing MAP estimation in the M step, rather than maximum likelihood.
 The ExpectationMaximization (EM) algorithm is an iterative approach to maximum likelihood parameter estimation.
 Dempster, A. P., Laird, N. and Rubin, D. B. (1977). Maximum likelihood estimation from incomplete data using the EM algorithm (with discussion).
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 Bock, R. D., & Aitkin, M. (1981). Marginal maximum likelihood estimation of item parameters: Application of an EM algorithm.
 TREEPUZZLE is a computer program to reconstruct phylogenetic trees from molecular sequence data by maximum likelihood.
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 The problem of restricted maximum likelihood (REML) estimation of covariance matrices with restricted parameter spaces is examined.
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 Specifically, when the shape parameter is less than 1, then a maximum likelihood solution does not exist for the parameters.
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 The gllamm software estimates generalized linear latent and mixed models by maximum likelihood using adaptive quadrature.
 This study showed the good not asymptotic properties of such estimators, which performed much better than the Maximum likelihood ones.
 Yuan, K.H. & Hayashi, K. (2005). On Muthen's maximum likelihood for twolevel covariance structure models.
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 Key words: Growth modeling, finite mixtures, latent variables, trajectory classes, maximum likelihood, nonparametric distribution.
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 Mestimators are a generalization of maximum likelihood estimators (MLEs).
 In other words, the maximum likelihood estimator is the value of theta that maximizes the probability of the observed sample.
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 A maximum likelihood estimate is the set of parameter values that maximize this function.
 Reviews maximum likelihood regression before going on to logit and tobit models for categorical dependents.
 The maximum likelihood function has been "worked out" for probit and logit regression models.
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 BHHH provides a method of estimating the asymptotic covariance matrix of a Maximum Likelihood Estimator.
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 The Fisher information is the main ingredient in the CramerRao lower bound, and in some maximum likelihood estimators.
 Maximum Likelihood Estimators are ofteh biased: the expectation of the estimator is not the right one.
 It is known that maximum likelihood estimators are asymptotically normally distributed.
 On Bahadur asymptotic efficiency of maximum likelihood estimator for a generalized semiparametric model.
 Maximum likelihood estimators and likelihood ratio tests, efficiency.
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 Paul, S.R. (1986). A note on maximum likelihood ratio test of no outliers in regression models.
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 EM is a partially nonBayesian, maximum likelihood method.
 Either the maximum likelihood estimate or the maximum a posteriori estimate may be used in place of the exact value in the above equations.
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 Ramsay, J. O. Maximum likelihood estimation in multidimensional scaling.
 The MAP demodulator 100 (FIG. 1) is a generalization of a standard maximum likelihood (ML) demodulator.
 Download (p018) Takane, Y. (1981). Multidimensional successive categories scaling: A maximum likelihood method.
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 In general, however, the maximum likelihood and least squares estimates need not be the same.
 Also, unlike OLS regression, logistic regression uses maximum likelihood estimation (MLE) rather than ordinary least squares (OLS) to derive parameters.
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 Note that in the model command, nogls suppresses generalized least squares estimation and ml specifies maximum likelihood estimation.
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 A maximum likelihood approach to nonlinear blind source separation.
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 Maximum likelihood for blind separation and deconvolution of noisy signals using mixture models.
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 This is the principle of maximum likelihood estimation.
 Information theory and an extension of the maximum likelihood principle.
 This opens the way to the maximum likelihood estimation of hidden semiMarkov chains from long sequences.
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 The main problem considered in the paper is studing properties of maximum likelihood estimators MLE's in certain mixed models with two variance components.
 For the Frechet upper bound, the twostage estimation procedure can sometimes be equivalent to maximum likelihood estimation for the univariate parameters.
 The maximum likelihood estimator is one effective implementation of the likelihood principle.
 This constrained NMF (cNMF) algorithm can be viewed as a maximum likelihood approach for finding basis vectors in a bounded subspace.
 Estimation of Markov random field prior parameters using Markov chain Monte Carlo maximum likelihood.
 From this example, it was found that the sample mean is the maximum likelihood estimator for N samples of a fixed, unknown parameter corrupted by AWGN.
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 The Fisher information is the main ingredient in the CramerRao lower bound, and in some maximum likelihood estimators.
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 The maximum likelihood estimator for this variance is constructed and it is proved that it is strongly consistent and asymptotically normal.
 When treating as the sample covariance matrix in the maximum likelihood (ML) procedure, consistent parameter estimates are still obtained.
 Standard approaches to estimate these parameters (known by the name Hyperparameters) are Maximum Likelihood (ML) and Maximum APosterior (MAP) approaches.
 The MLE (maximum likelihood estimate) of the parameters is solved via an EM algorithm [ 13].
 The maximum likelihood estimator (MLE) of a parameter θ can be used to calculate the MLE of a function of the parameter.
 Note that you are using the maximum likelihood estimate of the error variance under the null hypothesis.
 For probit and logit regression models, you may use maximum likelihood estimation (i.e., maximize the likelihood function).
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 For other problems, no maximum likelihood estimate exists (meaning that the loglikelihood function increases without attaining the supremum value).
 Boomsma, A. (1985). Nonconvergence, improper solutions, and starting values in LISREL maximum likelihood estimation.
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 To quantify the precision of the estimates, we'll use standard errors computed from the asymptotic covariance matrix of the maximum likelihood estimators.
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 For a model with normally distributed errors the method of least squares and the method of maximum likelihood coincide (see GaussMarkov theorem).
 GLS estimates are maximum likelihood estimates when ε follows a multivariate normal distribution with a known covariance matrix.
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 Censoring (statistics) Delta method, a method for finding the distribution of functions of a maximum likelihood estimator.
 Asymptotic properties of maximum likelihood estimators and likelihood ratio tests under nonstandard conditions.
 Stata has several commands that can be used to fit logistic regression models by maximum likelihood.
 For maximum likelihood estimations, a model may have a number of nuisance parameters.
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 One of the bestknown classes of robust techniques is that of Mestimators, which are an extension of the maximum likelihood estimation method.
 Summary: GCHap quickly finds maximum likelihood estimates (MLEs) of frequencies of haplotypes given genotype information on a random sample of individuals.
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 The consistency and the asymptotic normality of the maximum likelihood estimator in the general nonlinear simultaneous equation model are proved.
 The important fact here is that the standard error of a maximum likelihood estimator can be calculated.
 We obtain maximum likelihood estimates for all of the parameters and compare the results to standard probabilistic methods of parameter estimation.
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 Special attention is given to model specification, maximum likelihood estimation and microsimulation of tax reforms.
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 In this note we discuss the breakdown behavior of the maximum likelihood (ML) estimator in the logistic regression model.
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 The free parameters of the mixture model are estimated using a Maximum Likelihood Estimation (MLE) approach.
 The unknown parameters, β, are typically estimated with maximum likelihood, maximum quasilikelihood, or Bayesian techniques.
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 The coefficients of the logit model are estimated by the method of maximum likelihood estimation.
 EViews 5 features an object (the LogL) for handling userspecified maximum likelihood estimation problems.
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 Maximum likelihood estimators achieve minimum variance (as given by the CramerRao lower bound) in the limit as the sample size tends to infinity.
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 The sample mean is the minimum variance unbiased estimator (MVUE) in addition to being the maximum likelihood estimator.
 An EM algorithm delivers the maximum likelihood estimates of the remaining parameters.
 Isotonic regression is a maximum likelihood estimator under the assumption of a monotone dose response relationship.
 Three optimality criteria are available: maximum parsimony, distance, and maximum likelihood.
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 The model parameters can be found with maximum likelihood estimation using the EM algorithm.
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 The most commonlyused methods to infer phylogenies include parsimony, maximum likelihood, and MCMC based Bayesian inference.
 EMM produces larger confidence bounds than indirect inference and maximum likelihood, yet is much less likely to contain the true parameter value.
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 Felsenstein, J. 1973. Maximum likelihood estimation of evolutionary trees from continuous characters.
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 Maximum likelihood (ML) is increasingly used as an optimality criterion for selecting evolutionary trees (Felsenstein, 1981, J. Mol.
 The maximum likelihood method is used for GARCH models and for mixed ARGARCH models.
 Searches that use the distance criterion and the parsimony criterion are much faster than searches using maximum likelihood.
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Maximum Likelihood
 Also, unlike OLS regression, logistic regression uses maximum likelihood estimation (MLE) rather than ordinary least squares (OLS) to derive parameters.
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