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. |
主题分类 |
基礎與應用科學 >
資訊科學 基礎與應用科學 > 統計 |