A Flexible Multivariate Distribution for Correlated Count Data

A flexible distribution class for count data

*Correspondence: [email protected] 1Department of Mathematics and Statistics, Georgetown University, 306 St. Mary’s Hall, Washington, DC, 20057, USA Full list of author information is available at the end of the article Abstract The Poisson, geometric and Bernoulli distributions are special cases of a flexible count distribution, namely the Conway-Maxwell-Poisson (CMP) distribution – a two-pa...

A Flexible Regression Model for Count Data

Poisson regression is a popular tool for modeling count data and is applied in a vast array of applications from the social to the physical sciences and beyond. Real data, however, are often overor under-dispersed and, thus, not conducive to Poisson regression. We propose a regression model based on the Conway–Maxwell-Poisson (COM-Poisson) distribution to address this problem. The COM-Poisson r...

Latent feature regression for multivariate count data

We consider the problem of regression on multivariate count data and present a Gibbs sampler for a latent feature regression model suitable for both underand overdispersed response variables. The model learns countvalued latent features conditional on arbitrary covariates, modeling them as negative binomial variables, and maps them into the dependent count-valued observations using a Dirichlet-...

Screening for a Multivariate Mixture Normal Distribution

A Review of Multivariate Distributions for Count Data Derived from the Poisson Distribution.

The Poisson distribution has been widely studied and used for modeling univariate count-valued data. Multivariate generalizations of the Poisson distribution that permit dependencies, however, have been far less popular. Yet, real-world high-dimensional count-valued data found in word counts, genomics, and crime statistics, for example, exhibit rich dependencies, and motivate the need for multi...