MULTIPLE-INFLATION POISSON MODEL WITH L1 REGULARIZATION

被引：15

作者：

Su, Xiaogang ^{[1
]}

Fan, Juanjuan ^{[2
]}

Levine, Richard A. ^{[2
]}

Tan, Xianming ^{[3
]}

Tripathi, Arvind ^{[4
]}

机构：

[1] Univ Alabama Birmingham, Sch Nursing, Birmingham, AL 35294 USA

[2] San Diego State Univ, Dept Math & Stat, San Diego, CA 92182 USA

[3] McGill Univ, Ctr Hlth, Montreal, PQ H3H 2R9, Canada

[4] Univ Alabama Birmingham, Dept Biostat, Birmingham, AL 35294 USA

来源：

STATISTICA SINICA | 2013年 / 23卷 / 03期

基金：

美国国家科学基金会;

关键词：

Count data; LASSO; Poisson distribution; variable selection; zero-inflated; NONCONCAVE PENALIZED LIKELIHOOD; REGRESSION-MODELS; VARIABLE SELECTION; ORACLE PROPERTIES; LASSO;

D O I：

10.5705/ss.2012.187

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：

A multiple-inflation Poisson (MIP) model is put forward for analyzing count data that have multiple inflated values. Analogous to the zero-inflated Poisson model (ZIP; Lambert (1992)), MIP assumes a mixture distribution of Poisson and degenerate distributions, where the probabilities for the inflated values are from a cumulative logit model. We explore the properties of the proposed model, with a detailed treatment given to its maximum likelihood estimation. Moreover, we address variable selection by adopting an L-1 regularization scheme. Both simulation experiments and an analysis of a health care data set are provided to illustrate the multiple-inflation Poisson model.

引用

页码：1071 / 1090

页数：20

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