Regression trees for analysis of count data with extra Poisson variation

被引：13

作者：

Choi, Y

Ahn, H

Chen, JJ

机构：

[1] SUNY Stony Brook, Dept Appl Math & Stat, Stony Brook, NY 11794 USA

[2] Natl Ctr Toxicol Res, Div Biometry & Risk Assessment, Jefferson, AR 72079 USA

[3] Seoul Natl Univ, Dept Prevent Med, Sch Med, Chongro, South Korea

来源：

COMPUTATIONAL STATISTICS & DATA ANALYSIS | 2005年 / 49卷 / 03期

关键词：

carcinogenicity; generalized linear model; quasi-likelihood; recursive partitioning;

D O I：

10.1016/j.csda.2004.06.011

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

This article proposes methods for fitting piecewise loglinear models to count data with an extra-Poisson variation. Both SUPPORT (Statistica Sinica, 4 (1994) 143) and GUIDE (Statistica Sinica, 12 (2002) 361) are used for splitting methods. We developed a new bootstrap resampling method performed at each node of the tree to determine the proper size of a tree. The quasi-likelihood approach is used for fitting an extra-Poisson model at each stratum to take into account the extra variability. An adjusted Anscombe residual for the extra-Poisson model is used in this procedure. Performance of the proposed method is evaluated by a Monte Carlo simulation study. The proposed method is used to investigate geographic variability in mortality rates on lung cancer as well as effects of various demographic variability. (c) 2004 Elsevier B.V. All rights reserved.

引用

页码：893 / 915

页数：23

共 27 条

[1] Tree-structured logistic model for over-dispersed binomial data with application to modeling developmental effects [J].