Relative variation indexes for multivariate continuous distributions on [0,∞)k and extensions

We introduce some new indexes to measure the departure of any multivariate continuous distribution on the nonnegative orthant of the corresponding space from a given reference distribution. The reference distribution may be an uncorrelated exponential model. The proposed multivariate variation index...

Full description

Saved in:
Bibliographic Details
Published in:Advances in statistical analysis : AStA : a journal of the German Statistical Society 2020, Vol.104 (2), p.285-307
Main Authors: Kokonendji, Célestin C., Touré, Aboubacar Y., Sawadogo, Amadou
Format: Article
Language:English
Subjects:
Asymptotic properties
Covariance matrix
Econometrics
Economics
Finance
Insurance
Management
Mathematical analysis
Mathematics and Statistics
Matrix algebra
Matrix methods
Multivariate analysis
Original Paper
Probability Theory and Stochastic Processes
Quadratic forms
Statistics
Statistics for Business
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:We introduce some new indexes to measure the departure of any multivariate continuous distribution on the nonnegative orthant of the corresponding space from a given reference distribution. The reference distribution may be an uncorrelated exponential model. The proposed multivariate variation indexes that are a continuous analogue to the relative Fisher dispersion indexes of multivariate count models are also scalar quantities, defined as ratios of two quadratic forms of the mean vector to the covariance matrix. They can be used to discriminate between continuous positive distributions. Generalized and multiple marginal variation indexes with and without correlation structure, respectively, and their relative extensions are discussed. The asymptotic behaviors and other properties are studied. Illustrative examples as well as numerical applications are analyzed under several scenarios, leading to appropriate choices of multivariate models. Some concluding remarks and possible extensions are made.
ISSN:1863-8171
1863-818X
DOI:10.1007/s10182-020-00364-7