Influence Diagnostics for Linear Longitudinal Models
本文针对个体特定纵向模型，提出在固定和随机个体效应下的影响诊断方法，通过删除个体（一组相关观测）并开发部分影响统计量，帮助分析者理解个体观测对总体参数估计的联合影响，并以纳税人慈善捐赠数据为例说明应用。
Abstract 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.