Insurance and economic data are often positive, and we need to take into account this
peculiarity in choosing a statistical model for their distribution. An example is the inverse …

The Gaussian cluster-weighted model (CWM) is a mixture of regression models with random
covariates that allows for flexible clustering of a random vector composed of response …

ABSTRACT A correct modelization of the insurance losses distribution is crucial in the
insurance industry. This distribution is generally highly positively skewed, unimodal hump …

We propose a model-based clustering procedure where each component can take into
account cluster-specific mild outliers through a flexible distributional assumption, and a …

In statistical analysis, particularly in econometrics, the finite mixture of regression models
based on the normality assumption is routinely used to analyze censored data. In this work …

Interval-valued variables are required in data analysis since this type of data represents
either the uncertainty existing in an error measurement or the natural variability of the data …

Two families of parsimonious mixture models are introduced for model-based clustering.
They are based on two multivariate distributions-the shifted exponential normal and the tail …

Mixture regression models (MRMs) are widely used to capture the heterogeneity of
relationships between the response variable and one or more predictors coming from …

The research objective of this paper is to handle situations where the empirical distribution
of multivariate real-valued data is elliptical and with heavy tails. Many statistical models …

Mixture of linear experts (MoE) model is one of the widespread statistical frameworks for
modeling, classification, and clustering of data. Built on the normality assumption of the error …