A class of Item Response Theory (IRT) models for binary and ordinal polytomous items is illustrated and an R package for dealing with these models, named MultiLCIRT, is described. The models at issue extend traditional IRT models allowing for multidimensionality and discreteness of latent...

An original and meaningful probabilistic generative model for full rank data modelling is proposed. Rank data arise from a sorting mechanism which is generally unobservable for statisticians. Assuming that this process relies on paired comparisons, the insertion sort algorithm is known as being...

Recently, a new cure rate survival model has been proposed by considering the Conway–Maxwell Poisson distribution as the distribution of the competing cause variable. This model includes some of the well-known cure rate models discussed in the literature as special cases. Cancer clinical...

A robust estimation procedure for mixture linear regression models is proposed by assuming that the error terms follow a Laplace distribution. Using the fact that the Laplace distribution can be written as a scale mixture of a normal and a latent distribution, this procedure is implemented by an...

The Markov-switching GARCH model offers rich dynamics to model financial data. Estimating this path dependent model is a challenging task because exact computation of the likelihood is infeasible in practice. This difficulty led to estimation procedures either based on a simplification of the...

Recently, finite mixture models have been used to model the distribution of the error terms in multivariate linear regression analysis. In particular, Gaussian mixture models have been employed. A novel approach that assumes that the error terms follow a finite mixture of t distributions is...

Estimation of generalized linear mixed models (GLMMs) with non-nested random effects structures requires the approximation of high-dimensional integrals. Many existing methods are tailored to the low-dimensional integrals produced by nested designs. We explore the modifications that are required...

Several univariate proportional reversed hazard models have been proposed in the literature. Recently, Kundu and Gupta (2010) proposed a class of bivariate models with proportional reversed hazard marginals. It is observed that the proposed bivariate proportional reversed hazard models have a...

Markovian binary trees form a class of continuous-time branching processes where the lifetime and reproduction epochs of individuals are controlled by an underlying Markov process. An Expectation–Maximization (EM) algorithm is developed to estimate the parameters of the Markov process from the...

Semi-supervised classification can help to improve generative classifiers by taking into account the information provided by the unlabeled data points, especially when there are far more unlabeled data than labeled data. The aim is to select a generative classification model using both unlabeled...