MAXIMUM LIKELIHOOD NONLINEAR SYSTEM ESTIMATION

Maximum Likelihood Nonlinear System Estimation

This paper is concerned with the parameter estimation of a relatively general class of nonlinear dynamic systems. A Maximum Likelihood (ML) framework is employed in the interests of statistical efficiency, and it is illustrated how an Expectation Maximisation (EM) algorithm may be used to compute these ML estimates. An essential ingredient is the employment of so-called “particle smoothing” met...

Maximum Likelihood Estimation and Polynomial System Solving

This article presents an alternative method to find the global maximum likelihood estimates of the mixing probabilities of a mixture of multinomial distributions. For these mixture models it is shown that the maximum likelihood estimates of the mixing probabilities correspond with the roots of a multivariate polynomial system. A new algorithm, set in a linear algebra framework, is presented whi...

Maximum Likelihood Estimation of Parameters in Generalized Functional Linear Model

Sometimes, in practice, data are a function of another variable, which is called functional data. If the scalar response variable is categorical or discrete, and the covariates are functional, then a generalized functional linear model is used to analyze this type of data. In this paper, a truncated generalized functional linear model is studied and a maximum likelihood approach is used to esti...

Maximum Likelihood Estimation ∗ Clayton

This module introduces the maximum likelihood estimator. We show how the MLE implements the likelihood principle. Methods for computing th MLE are covered. Properties of the MLE are discussed including asymptotic e ciency and invariance under reparameterization. The maximum likelihood estimator (MLE) is an alternative to the minimum variance unbiased estimator (MVUE). For many estimation proble...

Maximum likelihood and Bayesian estimation for nonlinear structural equation models