A graphical method of exploring the mean structure in longitudinal data analysis

被引:2
|
作者
Faraway, JJ [1 ]
机构
[1] Univ Michigan, Dept Stat, Ann Arbor, MI 48109 USA
关键词
curve estimation; exploratory data analysis; functional data analysis; nonparametric regression; repeated measures;
D O I
10.2307/1390920
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
In a longitudinal study, individuals are observed over some period of time. The investigator wishes to model the responses over this time as a function of various covariates measured on these individuals. The times of measurement may be sparse and not coincident across individuals. When the covariate values are not extensively replicated, it is very difficult to propose a parametric model linking the response to the covariates because plots of the raw data are of little help. Although the response curve may only be observed at a few points, we consider the underlying curve y(t). We fit a regression model y(t) = x(T) beta(t) + epsilon(t) and use the coefficient functions beta(t) to suggest a suitable parametric form. Estimates of y(t) are constructed by simple interpolation, and appropriate weighting is used in the regression. We demonstrate the method on simulated data to show its ability to recover the true structure and illustrate its application to some longitudinal data from the Panel Study of Income Dynamics.
引用
收藏
页码:60 / 68
页数:9
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