.png){kind=icon}
返回
Influence diagnostics for linear longitudinal models
.png){kind=icon}
PRE
AI
DOI：10.2307/2965564.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Influence diagnostics are important for analyzing cross-sectional regression studies, because they allow the analyst to understand the impact of individual observations on the estimated regression model. In this article we consider the role of influence diagnostics in subject-specific longitudinal models. Diagnostics are proposed under both fixed and random subject effects. Our approach is based on subject deletion, which in this setting involves deleting a group of correlated observations. We develop partial influence statistics to understand the combined impact of observations from a subject on population parameters. Simple computational formulas make the procedures feasible. Finally, we illustrate the use of our new influence statistics by examining a dataset to model a taxpayer's charitable givings.
Keyword:
Cook's distance
partial influence
random effects
serial correlation
subject deletion
