NEW METHODS IN MAXIMUM LIKELIHOOD FACTOR ANALYSIS

Maximum Likelihood Parallel Factor Analysis (mlparafac)

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Maximum Likelihood in Generalized Fixed Score Factor Analysis

We study the weighted least squares fixed rank approximation problem in which the weight matrices depend on unknown parameters. The classical example is fixed score factor analysis (FSFA), where the weights depend on the unknown uniquenesses.

Maximum Likelihood Methods in Molecular Phylogenetics

Least squares methods in maximum likelihood problems

It is well known that the GaussNewton algorithm for solving nonlinear least squares problems is a special case of the scoring algorithm for maximizing log likelihoods. What has received less attention is that the computation of the current correction in the scoring algorithm in both its line search and trust region forms can be cast as a linear least squares problem. This is an important observ...

A Penalized Maximum Likelihood Approach to Sparse Factor Analysis

Factor analysis is a popular multivariate analysis method which is used to describe observed variables as linear combinations of hidden factors. In applications one usually needs to rotate the estimated factor loading matrix in order to obtain a more understandable model. In this article, an l1 penalization method is introduced for performing sparse factor analysis in which factor loadings natu...