Multivariate log-skewed distributions with normal kernel and their applications

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 ...

Multivariate t Distributions and Their Applications

Learning Multivariate Log-concave Distributions

We study the problem of estimating multivariate log-concave probability density functions. We prove the first sample complexity upper bound for learning log-concave densities on Rd, for all d ≥ 1. Prior to our work, no upper bound on the sample complexity of this learning problem was known for the case of d > 3. In more detail, we give an estimator that, for any d ≥ 1 and ǫ > 0, draws Õd ( (1/ǫ...

Multivariate Discrete Phase-Type Distributions with Their Applications

A dynamic random environment usually induces statistical dependence among components of a system operating in such an environment thereby making explicit computation of the system performance measures very difficult and possibly even intractable. The multivariate phase-type distribution is a powerful tool for unifying a variety of stochastic models and for constructing new models that yield to ...

Master Thesis Multivariate Skew-Normal Distributions and their Extremal Properties

In this master thesis we explore skewed distributions extended from the normal distribution. A transition from the symmetric to an asymmetric setting is formulated for the univariate as well as the multivariate case. This is done by introducing an additional infinite-dimensional parameter, namely a skewing function π. Our main focus is on the extremal behavior of such skewed random vectors. For...