Sparse Estimation of Multivariate Poisson Log-Normal Models from Count Data
This work addresses a challenging problem in statistics for researchers dealing with multivariate count data, offering a novel method that is incremental in extending existing univariate approaches.
The paper tackled the problem of modeling multivariate count data by proposing a multivariate Poisson log-normal regression model, which improved prediction performance by leveraging associations among responses, as demonstrated in simulations and real-world applications with systematic evaluations.
Modeling data with multivariate count responses is a challenging problem due to the discrete nature of the responses. Existing methods for univariate count responses cannot be easily extended to the multivariate case since the dependency among multiple responses needs to be properly accommodated. In this paper, we propose a multivariate Poisson log-normal regression model for multivariate data with count responses. By simultaneously estimating the regression coefficients and inverse covariance matrix over the latent variables with an efficient Monte Carlo EM algorithm, the proposed regression model takes advantages of association among multiple count responses to improve the model prediction performance. Simulation studies and applications to real world data are conducted to systematically evaluate the performance of the proposed method in comparison with conventional methods.