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.
机构:
Univ Tunku Abdul Rahman, Dept Math & Actuarial Sci, Sungai Long Campus, Kajang 43000, Selangor, MalaysiaUniv Tunku Abdul Rahman, Dept Math & Actuarial Sci, Sungai Long Campus, Kajang 43000, Selangor, Malaysia
Chia, Gek L.
Sim, Kai An
论文数: 0引用数: 0
h-index: 0
机构:
Sunway Univ, Sch Math Sci, Bandar Sunway 47500, Selangor, MalaysiaUniv Tunku Abdul Rahman, Dept Math & Actuarial Sci, Sungai Long Campus, Kajang 43000, Selangor, Malaysia