Modeling overdispersion with the normalized tempered stable distribution

A multivariate distribution which generalizes the Dirichlet distribution is introduced and its use for modeling overdispersion in count data is discussed. The distribution is constructed by normalizing a vector of independent tempered stable random variables. General formulae for all moments and cro...

Full description

Saved in:
Bibliographic Details
Published in:Computational statistics & data analysis 2011-07, Vol.55 (7), p.2288-2301
Main Authors: Kolossiatis, M., Griffin, J.E., Steel, M.F.J.
Format: Article
Language:English
Subjects:
Binomials
Computer simulation
Counting
Data processing
Dirichlet problem
Distribution on the unit simplex
Distribution on the unit simplex Mouse fetal mortality Mixed binomial Normalized random measure Overdispersion
Distribution theory
Exact sciences and technology
General topics
Mathematical analysis
Mathematics
Mixed binomial
Mouse fetal mortality
Multivariate analysis
Normalized random measure
Numerical analysis
Numerical analysis. Scientific computation
Numerical methods in probability and statistics
Overdispersion
Probability and statistics
Probability theory and stochastic processes
Sciences and techniques of general use
Statistics
Vectors (mathematics)
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:A multivariate distribution which generalizes the Dirichlet distribution is introduced and its use for modeling overdispersion in count data is discussed. The distribution is constructed by normalizing a vector of independent tempered stable random variables. General formulae for all moments and cross-moments of the distribution are derived and they are found to have similar forms to those for the Dirichlet distribution. The univariate version of the distribution can be used as a mixing distribution for the success probability of a binomial distribution to define an alternative to the well-studied beta-binomial distribution. Examples of fitting this model to simulated and real data are presented.
ISSN:0167-9473
1872-7352
DOI:10.1016/j.csda.2011.01.016