Maximum Likelihood Estimation for Spatial GLM Models

A comparison of algorithms for maximum likelihood estimation of Spatial GLM models

In spatial generalized linear mixed models, spatial correlation is assumed by adding normal latent variables to the model. In these models because of the non-Gaussian spatial response and the presence of latent variables the likelihood function cannot usually be given in a closed form, thus the maximum likelihood approach is very challenging. The main purpose of this paper is to introduce two n...

Maximum-likelihood estimation for multivariate spatial linear coregionalization models

A multivariate spatial linear coregionalization model is considered that incorporates the Matérn class of covariograms. An EM algorithm is developed for maximum-likelihood estimation that has a few desirable properties and is capable of handling high-dimensional data. Most estimates in the EM algorithm are updated through closed form expressions and these estimates automatically satisfy necessa...

Lecture 1: Maximum likelihood estimation of spatial regression models

Quasi Maximum Likelihood Estimation of Spatial Models with Heterogeneous Coefficients∗

This paper considers spatial autoregressive panel data models and extends their analysis to the case where the spatial coefficients differ across the spatial units. It derives conditions under which the spatial coefficients are identified and develops a quasi maximum likelihood (QML) estimation procedure. Under certain regularity conditions, it is shown that the QML estimators of individual spa...

Maximum-likelihood estimation for hidden Markov models

Hidden Markov models assume a sequence of random variables to be conditionally independent given a sequence of state variables which forms a Markov chain. Maximum-likelihood estimation for these models can be performed using the EM algorithm. In this paper the consistency of a sequence of maximum-likelihood estimators is proved. Also, the conclusion of the Shannon-McMillan-Breiman theorem on en...