Kullback proximal algorithms for maximum-likelihood estimation

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...

Kullback proximal algorithims for maximum-likelihood estimation

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...

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...

Space Alternating Penalized Kullback Proximal Point Algorithms for Maximing Likelihood with Nondifferentiable Penalty

The EM algorithm is a widely used methodology for penalized likelihood estimation. Provable monotonicity and convergence are the hallmarks of the EM algorithm and these properties are well established for smooth likelihood and smooth penalty functions. However, many relaxed versions of variable selection penalties are not smooth. The goal of this paper is to introduce a new class of Space Alter...