A flexible multivariate model for high-dimensional correlated count data
Journal of Statistical Distributions and Applications(2021)
Abstract
We propose a flexible multivariate stochastic model for over-dispersed count data. Our methodology is built upon mixed Poisson random vectors ( Y 1 ,…, Y d ), where the { Y i } are conditionally independent Poisson random variables. The stochastic rates of the { Y i } 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.
MoreTranslated text
Key words
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
AI Read Science
Must-Reading Tree
Example
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined