Nonparametric estimation of correlation functions in longitudinal and spatial data, with application to colon carcinogenesis experiments

TL;DR

This paper introduces a nonparametric kernel-based estimator for correlation functions in longitudinal and spatial data, accounting for inhomogeneity, with applications to colon carcinogenesis experiments and supporting simulation studies.

Contribution

It develops a novel nonparametric estimator for correlation functions in inhomogeneous spatial and longitudinal data, with asymptotic theory and inference methods.

Findings

Effective in analyzing colon carcinogenesis data

Performs well in simulation studies

Provides a practical inference framework

Abstract

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.