On a transform for modeling skewness

被引：3

作者：

Kang, Li ^{[1
]}

Damien, Paul ^{[2
]}

Walker, Stephen ^{[3
]}

机构：

[1] Univ Texas Austin, Dept Stat & Data Sci, Austin, TX 78712 USA

[2] Univ Texas Austin, McCombs Sch Business, Austin, TX 78712 USA

[3] Univ Texas Austin, Dept Math, Austin, TX 78712 USA

来源：

BRAZILIAN JOURNAL OF PROBABILITY AND STATISTICS | 2021年 / 35卷 / 02期

关键词：

Auxiliary variable; Bayesian inference; Markov chain Monte Carlo; mixtures of uniforms; SCALE MIXTURES; GENERAL-CLASS; DISTRIBUTIONS; INFERENCE;

D O I：

10.1214/20-BJPS477

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

In many applications, data exhibit skewness and in this paper we present a new family of density functions modeling skewness based on a transformation, analogous to those of location and scale. Here we note that location will always refer to mode. Hence, in order to model data to include shape, we need only to find a family of densities exhibiting a variety of shapes, since we can obtain the other three properties via the transformations. The chosen class of densities with the variety of shape is, we argue, the simplest available. Illustrations including regression and time series models are given.

引用

页码：335 / 350

页数：16

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