Open Access
August 2007 Nonparametric estimation of correlation functions in longitudinal and spatial data, with application to colon carcinogenesis experiments
Yehua Li, Naisyin Wang, Meeyoung Hong, Nancy D. Turner, Joanne R. Lupton, Raymond J. Carroll
Ann. Statist. 35(4): 1608-1643 (August 2007). DOI: 10.1214/009053607000000082


In longitudinal and spatial studies, observations often demonstrate strong correlations that are stationary in time or distance lags, and the times or locations of these data being sampled may not be homogeneous. We propose a nonparametric estimator of the correlation function in such data, using kernel methods. We develop a pointwise asymptotic normal distribution for the proposed estimator, when the number of subjects is fixed and the number of vectors or functions within each subject goes to infinity. Based on the asymptotic theory, we propose a weighted block bootstrapping method for making inferences about the correlation function, where the weights account for the inhomogeneity of the distribution of the times or locations. The method is applied to a data set from a colon carcinogenesis study, in which colonic crypts were sampled from a piece of colon segment from each of the 12 rats in the experiment and the expression level of p27, an important cell cycle protein, was then measured for each cell within the sampled crypts. A simulation study is also provided to illustrate the numerical performance of the proposed method.


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Yehua Li. Naisyin Wang. Meeyoung Hong. Nancy D. Turner. Joanne R. Lupton. Raymond J. Carroll. "Nonparametric estimation of correlation functions in longitudinal and spatial data, with application to colon carcinogenesis experiments." Ann. Statist. 35 (4) 1608 - 1643, August 2007.


Published: August 2007
First available in Project Euclid: 29 August 2007

zbMATH: 1147.62036
MathSciNet: MR2351099
Digital Object Identifier: 10.1214/009053607000000082

Primary: 62G08 , 62M10 , 91B72

Keywords: Asymptotic theory , bootstrap , colon carcinogenesis , correlation functions , dependent data , functional data , gene expression , kernel regression , Nonparametric regression , spatial data , time series

Rights: Copyright © 2007 Institute of Mathematical Statistics

Vol.35 • No. 4 • August 2007
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