Estimation and Model Selection in Dirichlet Regression

被引：10

作者：

Camargo, Andre P. ^{[1
]}

Stern, Julio M. ^{[1
]}

Lauretto, Marcelo S. ^{[2
]}

机构：

[1] Univ Sao Paulo, Inst Math & Stat, BR-05508 Sao Paulo, Brazil

[2] Univ Sao Paulo, Sch Arts Sci & Humanities, BR-05508 Sao Paulo, Brazil

来源：

BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING | 2012年 / 1443卷

基金：

巴西圣保罗研究基金会;

关键词：

Dirichlet regression; parameter estimation; model selection;

D O I：

10.1063/1.3703637

中图分类号：

O59 [应用物理学];

学科分类号：

摘要：

We study Compositional Models based on Dirichlet Regression where, given a (vector) covariate x, one considers the response variable y = (y(1), ... , y(D)) to be a positive vector with a conditional Dirichlet distribution, y vertical bar x similar to D (alpha(1) (x) ... alpha(D)(x)). We introduce a new method for estimating the parameters of the Dirichlet Covariate Model when alpha(j)(x) is a linear model on x, and also propose a Bayesian model selection approach. We present some numerical results which suggest that our proposals are more stable and robust than traditional approaches.

引用

页码：206 / 213

页数：8

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