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

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...

Comparison of Maximum Pseudo Likelihood and Maximum Likelihood Estimation of Exponential Family Random Graph Models

The statistical modeling of social network data is difficult due to the complex dependence structure of the tie variables. Statistical exponential families of distributions provide a flexible way to model such dependence. They enable the statistical characteristics of the network to be encapsulated within an exponential family random graph (ERG) model. For a long time, however, likelihood-based...

Kullback Proximal Algorithms for Maximum Likelihood Estimation

Accelerated algorithms for maximum likelihood image reconstruction are essential for emerging applications such as 3D tomography, dynamic tomographic imaging, and other high dimensional inverse problems. In this paper, we introduce and analyze a class of fast and stable sequential optimization methods for computing maximum likelihood estimates and study its convergence properties. These methods...

On Optimization Algorithms for Maximum Likelihood Estimation

Maximum likelihood estimation (MLE) is one of the most popular technique in econometric and other statistical applications due to its strong theoretical appeal, but can lead to numerical issues when the underlying optimization problem is solved. We examine in this paper a range of trust region and line search algorithms and focus on the impact that the approximation of the Hessian matrix has on...