120 research outputs found
Clustering Via Nonparametric Density Estimation: the R Package pdfCluster
The R package pdfCluster performs cluster analysis based on a nonparametric
estimate of the density of the observed variables. After summarizing the main
aspects of the methodology, we describe the features and the usage of the
package, and finally illustrate its working with the aid of two datasets
Statistical applications of the multivariate skew-normal distribution
Azzalini & Dalla Valle (1996) have recently discussed the multivariate
skew-normal distribution which extends the class of normal distributions by the
addition of a shape parameter. The first part of the present paper examines
further probabilistic properties of the distribution, with special emphasis on
aspects of statistical relevance. Inferential and other statistical issues are
discussed in the following part, with applications to some multivariate
statistics problems, illustrated by numerical examples. Finally, a further
extension is described which introduces a skewing factor of an elliptical
density.Comment: full-length version of the published paper, 32 pages, with 7 figures,
uses psfra
The information matrix of the bivariate extended skew-normal distribution
For the extended skew-normal distribution, which represents an extension of
the normal (or Gaussian) distribution, we focus on the properties of the
log-likelihood function and derived quantities in the the bivariate case.
Specifically, we derive explicit expressions for the score function and the
information matrix, in the observed and the expected form; these do not appear
to have been examined before in the literature. Corresponding computing code in
R language is provided, which implements the formal expressions
Some properties of the unified skew-normal distribution
For the family of multivariate probability distributions variously denoted as
unified skew-normal, closed skew-normal and other names, a number of properties
are already known, but many others are not, even some basic ones. The present
contribution aims at filling some of the missing gaps. Specifically, the
moments up to the fourth order are obtained, and from here the expressions of
the Mardia's measures of multivariate skewness and kurtosis. Other results
concern the property of log-concavity of the distribution, and closure with
respect to conditioning on intervals
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