Multivariate count data are common in many disciplines. The variables in such data often exhibit complex positive or negative dependency structures. We propose three Bayesian approaches to modeling bivariate count data by simultaneously considering covariate‐dependent means and correlation. A direct approach utilizes a bivariate negative binomial probability mass function developed in Famoye (2010, Journal of Applied Statistics). The second approach fits bivariate count data indirectly using a bivariate Poisson‐gamma mixture model. The third approach is a bivariate Gaussian copula model. Based on the results from simulation analyses, the indirect and copula approaches perform better overall than the direct approach in terms of model fitting and identifying covariate‐dependent association. The proposed approaches are applied to two RNA‐sequencing data sets for studying breast cancer and melanoma (BRCA‐US and SKCM‐US), respectively, obtained through the International Cancer Genome Consortium.

中文翻译：

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灵活的双变量相关计数数据回归。

多元计数数据在许多学科中很常见。这种数据中的变量通常表现出复杂的正或负依赖性结构。通过同时考虑依赖于协变量的均值和相关性，我们提出了三种贝叶斯方法来建模双变量计数数据。直接方法利用在Famoye（2010，Journal of Applied Statistics）中开发的双变量负二项式概率质量函数）。第二种方法是使用双变量Poisson-gamma混合模型间接拟合双变量计数数据。第三种方法是双变量高斯copula模型。根据仿真分析的结果，在模型拟合和识别协变量相关的关联方面，间接和copula方法的整体效果要好于直接方法。建议的方法分别应用于通过国际癌症基因组协会获得的两个用于研究乳腺癌和黑色素瘤的RNA测序数据集（BRCA-US和SKCM-US）。