Skip Navigation

Biometrika 2003 90(1):43-52; doi:10.1093/biomet/90.1.43
© 2003 by Biometrika Trust
This Article
Right arrow Full Text (PDF)
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Wang, N.
Right arrow Search for Related Content
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

Marginal nonparametric kernel regression accounting for within-subject correlation

Naisyin Wang1

1 Department of Statistics, Texas A&M University, College Station, Texas 77843-3143, U.S.Anwang{at}stat.tamu.edu

There has been substantial recent interest in non- and semiparametric methods for longitudinal or clustered data with dependence within clusters.It has been shown rather inexplicably that, when standard kernel smoothing methods are used in a natural way, higher efficiency is obtained by assuming independence than by using the true correlation structure. It is shown here that this result is a natural consequence of how standard kernel methods incorporate the within-subject correlation in the asymptotic setting considered, where the cluster sizes are fixed and the cluster number increases. In this paper, an alternative kernel smoothing method is proposed. Unlike the standard methods, the smallest variance of the new estimator is achieved when the true correlation is assumed. Asymptotically, the variance of the proposed method is uniformly smaller than that of the most efficient working independence approach. A small simulation study shows that significant improvement is obtained for finite samples.

Key Words: Asymptotic relative efficiency: Asymptotics; Bandwidth: Generalised estimating equation; Local linear estimator: Working covariance matrix; Working independence estimator

Received August 2001. Revised May 2002


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
 BiometrikaHome page
Z. Zhu, W. K. Fung, and X. He
On the asymptotics of marginal regression splines with longitudinal data
Biometrika, December 1, 2008; 95(4): 907 - 917.
[Abstract] [PDF]

Home page
 BiometrikaHome page
Z. Yu and X. Lin
Nonparametric regression using local kernel estimating equations for correlated failure time data
Biometrika, March 1, 2008; 95(1): 123 - 137.
[Abstract] [PDF]

Home page
 BiometrikaHome page
L. Xue and L. Zhu
Empirical Likelihood Semiparametric Regression Analysis for Longitudinal Data
Biometrika, December 1, 2007; 94(4): 921 - 937.
[Abstract] [PDF]


Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.