Family of mean-mixtures of multivariate normal distributions: Properties, inference and assessment of multivariate skewness

Comparing Mean Vectors Via Generalized Inference in Multivariate Log-Normal Distributions

Abstract In this paper, we consider the problem of means in several multivariate log-normal distributions and propose a useful method called as generalized variable method. Simulation studies show that suggested method has a appropriate size and power regardless sample size. To evaluation this method, we compare this method with traditional MANOVA such that the actual sizes of the two methods ...

Inference for multivariate normal mixtures

Multivariate normal mixtures provide a flexible model for high-dimensional data. They are widely used in statistical genetics, statistical finance, and other disciplines. Due to the unboundedness of the likelihood function, classical likelihoodbased methods, which may have nice practical properties, are inconsistent. In this paper, we recommend a penalized likelihood method for estimating the m...

Shape mixtures of multivariate skew-normal distributions

Classes of shape mixtures of independent and dependent multivariate skew-normal distributions are considered and some of their main properties are studied. If interpreted from a Bayesian point of view, the results obtained in this paper bring tractability to the problem of inference for the shape parameter, that is, the posterior distribution can be written in analytic form. Robust inference fo...

Characteristic functions of scale mixtures of multivariate skew-normal distributions

some properties of fuzzy hilbert spaces and norm of operators

in this thesis, at first we investigate the bounded inverse theorem on fuzzy normed linear spaces and study the set of all compact operators on these spaces. then we introduce the notions of fuzzy boundedness and investigate a new norm operators and the relationship between continuity and boundedness. and, we show that the space of all fuzzy bounded operators is complete. finally, we define...