Regression models for bivariate count outcomes

被引：31
作者：
Xu, Xinling [1 ]
Hardin, James W. [1 ]
机构：
[1] Univ S Carolina, Dept Epidemiol & Biostat, Columbia, SC 29208 USA
来源：
STATA JOURNAL | 2016年 / 16卷 / 02期
关键词：
st0433; bivcnto; copula function; correlated count data; Poisson; negative binomial; Famoye bivariate Poisson regression; Marshall-Olkin bivariate negative binomial regression; Famoye bivariate negative binomial regression; Famoye bivariate generalized Poisson regression; general bivariate count regression; NEGATIVE BINOMIAL REGRESSION; DISTRIBUTIONS;
D O I：
10.1177/1536867X1601600203
中图分类号：
O1 [数学]; C [社会科学总论];
学科分类号：
03 ; 0303 ; 0701 ; 070101 ;
摘要：
We present a new command, bivcnto, for fitting regression models suitable for analyzing correlated count outcomes. bivcnto allows specification of two correlated count outcomes with either two outcome-specific covariate lists or one common covariate list and fits models using a copula function approach in the general case or using specific parameterizations by Marshall and Olkin (1985, Journal of the American Statistical Association 80: 332-338) or Famoye (2010a, Journal of Applied Statistics 37: 969-981; 2010b, Statistica Neerlandica 64: 112-124). bivcnto also calculates a likelihood-ratio test comparing the joint model with estimation of two independent outcome-specific models.
引用
收藏
页码：301 / 315
页数：15
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