A0700
Title: Bayesian period-varying Poisson modeling with reduced-rank regression
Authors: Myungjin Kim - Kyungpook National University (Korea, South) [presenting]
Jieun Lee - Kyungpook National University (Korea, South)
Yongku Kim - Kyungpook National University (Korea, South)
Abstract: Poisson regression models have been a classical tool for estimating and predicting species abundance. However, as changes in weather conditions become more frequent and dramatic, Poisson models that can reflect varying impacts of climate variables with respect to time are needed. The application domain of Poisson regression models is expanded by developing a period-varying Poisson regression framework that can explain the varying impacts of covariates on the outcome over the course of time. The framework of period-varying Poisson regression modeling uses the notion of reduced-rank regression, which improves the accuracy of statistical inference by allowing the regression coefficient vectors to share information with each other. The consistency in rank selection for the proposed method is established. To enable statistical inference under the low-rank constraint, the weighted Bayesian bootstrap is incorporated into the framework. The results of a simulation study are provided to demonstrate the performance of the proposed method. Finally, the period-varying Poisson regression model is applied to investigate the effect of varying climates on the mosquito population in a rural city in South Korea.
