Bayesian composite $$L^p$$ -quantile regression

Pub Date : 2024-02-21 DOI:10.1007/s00184-024-00950-8

引用次数: 0

Abstract

$L^p$ -quantiles are a class of generalized quantiles defined as minimizers of an asymmetric power function. They include both quantiles, $p=1$ , and expectiles, $p=2$ , as special cases. This paper studies composite $L^p$ -quantile regression, simultaneously extending single $L^p$ -quantile regression and composite quantile regression. A Bayesian approach is considered, where a novel parameterization of the skewed exponential power distribution is utilized. Further, a Laplace prior on the regression coefficients allows for variable selection. Through a Monte Carlo study and applications to empirical data, the proposed method is shown to outperform Bayesian composite quantile regression in most aspects.

查看原文

贝叶斯综合 $$L^p$$ -quantile 回归

Abstract $L^p$ -quantiles 是一类定义为非对称幂函数最小化的广义量值。它们包括量值（p=1）和期望值（p=2）作为特例。本文研究了复合量值回归，同时扩展了单一量值回归和复合量值回归。研究考虑了一种贝叶斯方法，利用了倾斜指数幂分布的新参数化。此外，回归系数的拉普拉斯先验允许进行变量选择。通过蒙特卡罗研究和对经验数据的应用，证明所提出的方法在大多数方面优于贝叶斯复合量化回归。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文