A flexible bivariate distribution for count data expressing data dispersion

被引：2

作者：

Weems, Kimberly S. ^{[1
]}

Sellers, Kimberly F. ^{[2
,3
]}

Li, Tong ^{[2
]}

机构：

[1] North Carolina Cent Univ, Dept Math & Phys, Durham, NC 27707 USA

[2] Georgetown Univ, Dept Math & Stat, Washington, DC USA

[3] US Census Bur, Ctr Stat Res & Methodol, Washington, DC USA

来源：

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS | 2023年 / 52卷 / 13期

基金：

美国国家科学基金会;

关键词：

Bivariate count data; Conway-Maxwell-Poisson distribution; trivariate reduction; POISSON; MODEL; FAMILY;

D O I：

10.1080/03610926.2021.1999474

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

The bivariate Poisson distribution is a natural choice for modeling bivariate count data. Its constraining assumption, however, limits model flexibility in some contexts. This work considers the trivariate reduction method to construct a Bivariate Conway-Maxwell-Poisson (BCMP) distribution, which accommodates over- and under-dispersed data. The approach produces marginals that have a flexible form which includes several special case distributions for certain parameters. Moreover, this BCMP model performs well relative to other bivariate models for count data, including BCMP models based on different methods of construction. As a result, the trivariate-reduced BCMP distribution is a flexible alternative for modeling bivariate count data containing data dispersion.

引用

页码：4692 / 4718

页数：27

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