Bayesian Quantile Regression with Subset Selection: A Posterior Summarization Perspective

arXiv:2311.02043v2 Announce Type: replace-cross
Abstract: Quantile regression is a powerful tool for inferring how covariates affect specific percentiles of the response distribution. Existing methods either estimate conditional quantiles separately for each quantile of interest or estimate the entire conditional distribution using semi- or non-parametric models. The former often produce inadequate models for real data and do not share information across quantiles, while the latter are characterized by complex and constrained models that can be difficult to interpret and computationally inefficient. …

abstract arxiv bayesian distribution math.st non-parametric parametric perspective posterior quantile regression semi stat.ap stat.co stat.me stat.ml stat.th summarization tool type