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 ...

Abstract: The settling/rising velocity is of key importance in the vertical distribution of microplastics in marine environment. It is generally parameterized with semi-empirical laws dependent on the ...

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 ...

Sampling from probability distributions with known density functions (up to normalization) is a fundamental challenge across various scientific domains. From Bayesian uncertainty quantification to ...

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 ...

This application provides an intuitive interface for calculating four fundamental discrete probability distributions commonly used in statistics and probability theory. Built with modern Java Swing ...

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, ...