English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

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

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