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June 2011 Two-stage empirical likelihood for longitudinal neuroimaging data
Xiaoyan Shi, Joseph G. Ibrahim, Jeffrey Lieberman, Martin Styner, Yimei Li, Hongtu Zhu
Ann. Appl. Stat. 5(2B): 1132-1158 (June 2011). DOI: 10.1214/11-AOAS480

Abstract

Longitudinal imaging studies are essential to understanding the neural development of neuropsychiatric disorders, substance use disorders, and the normal brain. The main objective of this paper is to develop a two-stage adjusted exponentially tilted empirical likelihood (TETEL) for the spatial analysis of neuroimaging data from longitudinal studies. The TETEL method as a frequentist approach allows us to efficiently analyze longitudinal data without modeling temporal correlation and to classify different time-dependent covariate types. To account for spatial dependence, the TETEL method developed here specifically combines all the data in the closest neighborhood of each voxel (or pixel) on a 3-dimensional (3D) volume (or 2D surface) with appropriate weights to calculate adaptive parameter estimates and adaptive test statistics. Simulation studies are used to examine the finite sample performance of the adjusted exponential tilted likelihood ratio statistic and TETEL. We demonstrate the application of our statistical methods to the detection of the difference in the morphological changes of the hippocampus across time between schizophrenia patients and healthy subjects in a longitudinal schizophrenia study.

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Xiaoyan Shi. Joseph G. Ibrahim. Jeffrey Lieberman. Martin Styner. Yimei Li. Hongtu Zhu. "Two-stage empirical likelihood for longitudinal neuroimaging data." Ann. Appl. Stat. 5 (2B) 1132 - 1158, June 2011. https://doi.org/10.1214/11-AOAS480

Information

Published: June 2011
First available in Project Euclid: 13 July 2011

zbMATH: 1223.62024
MathSciNet: MR2849769
Digital Object Identifier: 10.1214/11-AOAS480

Rights: Copyright © 2011 Institute of Mathematical Statistics

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Vol.5 • No. 2B • June 2011
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