Clustering is the task of automatically gathering observations into homogeneous groups,
where the number of groups is unknown. Through its basis in a statistical modeling …

The important role of finite mixture models in the statistical analysis of data is underscored
by the ever-increasing rate at which articles on mixture applications appear in the statistical …

The integrated nested Laplace approximation (INLA) is a recent computational method that
can fit Bayesian models in a fraction of the time required by typical Markov chain Monte …

On reading the first lines of the preface, the reader gets a clear picture of the book; it is about
modeling and learning using GAMLSS: generalized additive models for location, scale, and …

History Markov chain Monte Carlo (MCMC) methods, in which plausible values for unknown
quantities are simulated from their appropriate probability distribution, have revolutionised …

We show that epigenome-and transcriptome-wide association studies (EWAS and TWAS)
are prone to significant inflation and bias of test statistics, an unrecognized phenomenon …

This paper aims to model the COVID-19 mortality rates in Italy, Mexico, and the Netherlands,
by specifying an optimal statistical model to analyze the mortality rate of COVID-19. A new …

General state-space Markov chain theory has evolved to make it both more accessible and
more powerful. Markov Chain Monte Carlo in Practice introduces MCMC methods and their …

Data mining is the discovery of interesting, unexpected or valuable structures in large
datasets. As such, it has two rather different aspects. One of these concerns large …

A crucial problem in building a multiple regression model is the selection of predictors to
include. The main thrust of this article is to propose and develop a procedure that uses …