Longitudinal data analysis using sufficient dimension reduction method
There have been an increasing number of applications where the number of predictors is large, meanwhile data are repeatedly measured at a sequence of time points. In this article we investigate how dimension reduction method can be employed for analyzing such high-dimensional longitudinal data. Predictor dimension can be effectively reduced while full regression means information can be retained during dimension reduction. Simultaneous variable selection along with dimension reduction is studied, and graphical diagnosis and model fitting after dimension reduction are investigated. The method is flexible enough to encompass a variety of commonly used longitudinal models.
Year of publication: |
2009
|
---|---|
Authors: | Li, Lexin ; Yin, Xiangrong |
Published in: |
Computational Statistics & Data Analysis. - Elsevier, ISSN 0167-9473. - Vol. 53.2009, 12, p. 4106-4115
|
Publisher: |
Elsevier |
Saved in:
Online Resource
Saved in favorites
Similar items by person
-
The authors replied as follows:
Li, Lexin, (2008)
-
Sliced Inverse Regression with Regularizations
Li, Lexin, (2008)
-
Estimation of inverse mean: An orthogonal series approach
Wang, Qin, (2011)
- More ...