题名

Least square and Empirical Bayes Approaches for Estimating Random Change Points

DOI

10.6339/JDS.2009.07(1).444

作者

Yuan-Jia Wang;Yi-Xin Fang

关键词

Longitudinal data ; mixed effects model ; random change points model

期刊名称

Journal of Data Science

卷期/出版年月

7卷1期(2009 / 01 / 01)

页次

1 - 12

内容语文

英文

英文摘要

Here we develop methods for applications where random change points are known to be present a priori and the interest lies in their estimation and investigating risk factors that influence them. A simple least-square method estimating each individual's change point based on one's own observations is first proposed. An easy-to-compute empirical Bayes type shrinkage is then proposed to pool information from separately estimated change points. A method to improve the empirical Bayes estimates is developed. Simulations are conducted to compare least-square estimates and Bayes shrinkage estimates. The proposed methods are applied to the Berkeley Growth Study data to estimate the transition age of the puberty height growth.

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