The MM (minorization—maximization) principle is a versatile tool for constructing optimization algorithms. Every EM algorithm is an MM algorithm but not vice versa. This article derives MM algorithms ...
A rich class of parametric models is proposed for discrete choice data based on the scale mixture of multivariate normal distributions. The multinomial probit model is a special case in the class. The ...
We propose a family of copula-based multivariate distributions with g-and-h marginals. After studying the properties of the distribution, we develop a two-step estimation strategy and analyze via ...
ABSTRACT: In a previous article, an R script was developed and divided into three parts to implement the multivariate normality (MVN) Q-test based on both the chi-square approximation and the ...
This package, elliptical-distribution-toolkit, provides a variety of functions that deal with multivariate elliptical distributions. Two key distributions are the multivariate (mv) Gaussian and mv ...
context: Simulation for statistical power and sample size computation for regression (linear, GLM, discrete,...) when regressors are random and correlated. For each simulation run we also need random ...
I am generally interested in the development of statistical methods motivated by applications to real scientific problems. I have numerous areas of research, including: computational statistics, ...
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