Bayesian Variable Selection in Distributed Lag Models: A Focus on Binary Quantile and Count Data Regressions
分布滞后模型的贝叶斯变量选择:二元分位数与计数回归
Distributed Lag Models (DLMs) and similar regression approaches such as MIDAS have been used for many decades in econometrics and more recently to investigate how poor air quality adversely affects human health. In this paper we describe how to expand the utility of these models for Bayesian inference by leveraging latent variables. In particular we explain how to perform binary regression to better handle imbalanced data, how to incorporate negative binomial regression, and…