A flexible multivariate model for high-dimensional correlated count data

Journal of Statistical Distributions and Applications

A. Grant Schissler¹  Tomasz J. Kozubowski¹  Alexander D. Knudson¹  Anna K. Panorska¹ 

[1] Department of Mathematics & Statistics, University of Nevada, 89557, Reno, USA;

关键词: Multivariate count data;    Copula;    Distribution theory;    Big data applications;    Gamma-Poisson hierarchy;    Mixed Poisson distribution;    Negative binomial distribution;    High-dimensional multivariate simulation;    RNA-sequencing data;    62E10;    62E15;    62H05;    62H10;    62H30;   

DOI  :  10.1186/s40488-021-00119-y

来源: Springer

【 摘 要 】

We propose a flexible multivariate stochastic model for over-dispersed count data. Our methodology is built upon mixed Poisson random vectors (Y1,…,Yd), where the {Yi} are conditionally independent Poisson random variables. The stochastic rates of the {Yi} are multivariate distributions with arbitrary non-negative margins linked by a copula function. We present basic properties of these mixed Poisson multivariate distributions and provide several examples. A particular case with geometric and negative binomial marginal distributions is studied in detail. We illustrate an application of our model by conducting a high-dimensional simulation motivated by RNA-sequencing data.

【 授权许可】

CC BY