In this paper we introduce an estimator based on Likelihood Disparity which is a distance between distributions based on the Likelihood. This estimator is used to estimate location, scale and shape parameters of the skew normal distribution. Maximum Likelihood, Method of Moments and others estimation methods are often unreliable estimators of the shape parameter (they are biased towards infinity), especially for small sample size. Our estimator performs very well for all sample sizes and shape values. Its behavior is similar to the Maximum Likelihood estimator when the latter is finite, but provides a finite estimate in all remain cases
In many practical applications it has been observed that real data sets are not symmetric. They exhi...
Title: Skew normal distribution Author: František Helebrand Department: Department of Probability an...
The skew-normal model is a class of distributions that extends the Gaussian family by including a sk...
The normal distribution is symmetric and enjoys many important properties. That is why it is widely ...
The skew normal model is a class of distributions that extends the normal one by including a shape p...
The skew normal model is a class of distributions that extends the Gaussian family by including a sh...
The skew-normal and the skew-t distributions are parametric families which are currently under inten...
This work deals with testing a hypothesis on the location parameter (μ) of a skew-normal distributio...
This paper deals with the issue of performing a default Bayesian analysis on the shape parameter of ...
This paper introduces the scale-shape mixtures of skew-normal (SSMSN) distributions which provide al...
Parameter estimation for the skew-normal distribution is challenging, since the profile likelihood f...
Parameter estimation for the skew-normal distribution is challenging, since the profile likelihood f...
Classes of shape mixtures of independent and dependent multivariate skew-normal distributions are co...
In this paper we discuss a Bayesian analysis of the scalar skew-normal model. This model defines a c...
The aim of this paper is to discuss a scalar posterior distribution for the shape parameter k of the...
In many practical applications it has been observed that real data sets are not symmetric. They exhi...
Title: Skew normal distribution Author: František Helebrand Department: Department of Probability an...
The skew-normal model is a class of distributions that extends the Gaussian family by including a sk...
The normal distribution is symmetric and enjoys many important properties. That is why it is widely ...
The skew normal model is a class of distributions that extends the normal one by including a shape p...
The skew normal model is a class of distributions that extends the Gaussian family by including a sh...
The skew-normal and the skew-t distributions are parametric families which are currently under inten...
This work deals with testing a hypothesis on the location parameter (μ) of a skew-normal distributio...
This paper deals with the issue of performing a default Bayesian analysis on the shape parameter of ...
This paper introduces the scale-shape mixtures of skew-normal (SSMSN) distributions which provide al...
Parameter estimation for the skew-normal distribution is challenging, since the profile likelihood f...
Parameter estimation for the skew-normal distribution is challenging, since the profile likelihood f...
Classes of shape mixtures of independent and dependent multivariate skew-normal distributions are co...
In this paper we discuss a Bayesian analysis of the scalar skew-normal model. This model defines a c...
The aim of this paper is to discuss a scalar posterior distribution for the shape parameter k of the...
In many practical applications it has been observed that real data sets are not symmetric. They exhi...
Title: Skew normal distribution Author: František Helebrand Department: Department of Probability an...
The skew-normal model is a class of distributions that extends the Gaussian family by including a sk...