Finite mixture modeling of censored and missing data using the multivariate skew-normal distribution

Finite mixture modeling of censored data using the multivariate Student-t distribution

Finite mixture models have been widely used for the modelling and analysis of data from a heterogeneous population. Moreover, these kind of data can be subjected to some upper and/or lower detection limits because of the restriction of experimental apparatus. Another complication arises when measures of each population depart significantly from normality, for instance, in the presence of heavy ...

Finite Mixture Distributions with Skew Normal Components

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Comparing the Efficiency of Dmus with Normal and Skew-Normal Distribution using Data Envelopment Analysis

  Data envelopment analysis (DEA) is a nonparametric approach to evaluate theefficiency of decision making units (DMU) using mathematical programmingtechniques. Almost, all of the previous researches in stochastic DEA have been usedthe stochastic data when the inputs and outputs are normally distributed. But, thisassumption may not be true in practice. Therefore, using a normal distribution wi...

Determination of the number of components in finite mixture distribution with Skew-t-Normal components

Abstract One of the main goal in the mixture distributions is to determine the number of components. There are different methods for determination the number of components, for example, Greedy-EM algorithm which is based on adding a new component to the model until satisfied the best number of components. The second method is based on maximum entropy and finally the third method is based on non...

Screening for a Multivariate Mixture Normal Distribution