In this work we trait the problem of mixtures of normal distributions and methods for estimating the number of components as well as the parameters in a mixture. Also, we present a practical method for the detection of normal finite mixture distribution and respective model validation. Finally, we apply the exposed procedure to a sample of old grape-vine castes.

Shannon and Rényi entropies are two important measures of uncertainty for data analysis. These have been studied multivariate Student-t skew-normal distributions. In this paper, we extend the entropy to skew-t finite mixture (FMST) This class flexible distributions allows handling asymmetry tail weight behavior simultaneously. We find upper lower bounds these families. Numerical simulations ill...

Empirical models that relate multiple quality features to a set of design variables play vital role in many industrial process optimization methods. Many the current modeling methods employ single-response normal model analyze processes without taking into consideration high correlations and non-normality among response variables. Also, problem variable selection has also not yet been fully inv...

Modeling environmental data plays a crucial role in explaining phenomena. In some cases, well-known distributions, e.g., Weibull, inverse and Gumbel cannot model events adequately. Therefore, many authors tried to find new statistical distributions represent phenomena more accurately. this paper, an α-monotone generalized log-Moyal (α-GlogM) distribution is introduced properties such as cumulat...

This paper describes a flexible non-Gaussian statistical method used to model polarimetric synthetic aperture radar (POLSAR) data. We outline the theoretical basis of the well-know product model as described by the class of Scale Mixture models and discuss their appropriateness for modelling radar data. The statistical distributions of several Scale mixture models are then described, including ...

We investigate stochastic comparisons between exponential family distributions and their mixtures with respect to the usual stochastic order, the hazard rate order, the reversed hazard rate order, and the likelihood ratio order. A general theorem based on the notion of relative log-concavity is shown to unify various specific results for the Poisson, binomial, negative binomial, and gamma distr...

We investigate stochastic comparisons between exponential family distributions and their mixtures with respect to the usual stochastic order, the hazard rate order, the reversed hazard rate order, and the likelihood ratio order. A general theorem based on the notion of relative log-concavity is shown to unify various specific results for the Poisson, binomial, negative binomial, and gamma distr...