TY - JOUR
T1 - Intrinsic regression models for medial representation of subcortical structures
AU - Shi, Xiaoyan
AU - Zhu, Hongtu
AU - Ibrahim, Joseph G.
AU - Liang, Faming
AU - Lieberman, Jeffrey
AU - Styner, Martin
N1 - Funding Information:
Xiaoyan Shi is a statistician in the Advanced Analytics Division of SAS Institute Inc., 100 SAS Campus Drive, Cary, NC 27513-2414 (E-mail: amy.shi@sas.com). Hongtu Zhu, is Professor of Biostatistics, Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7420 (E-mail: hzhu@bios.unc.edu). Joseph G. Ibrahim is Alumni Distinguished Professor of Biostatistics, Department of Biostatistics and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7420 (E-mail: ibrahim@bios.unc.edu). Faming Liang is Professor of Statistics, Department of Statistics, Texas A&M University, College Station, TX 77843-3143 (E-mail: fliang@stat.tamu.edu). Jeffrey Lieberman is Lawrence C. Kolb Professor of Psychiatry, Department of Psychiatry, Columbia University Medical Center, New York, NY 10032 (E-mail: jlieberman@pi.cpmc.Columbia.edu). Martin Styner is Assistant Professor, Department of Computer Science and Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 (E-mail: styner@unc.edu). This work was supported in part by NIH grants UL1-RR025747-01, R21AG033387, P01CA142538-01, MH086633, GM 70335, and CA 74015 to Drs. Zhu and Ibrahim; DMS-1007457 and DMS-1106494 to Dr. Liang; and Lilly Research Laboratories, the UNC NDRC HD 03110, Eli Lilly grant F1D-MC-X252, and NIH Roadmap grant U54 EB005149-01, NAMIC to Dr. Styner. We thank the editor, an associate editor, and two referees for helpful suggestions, which improved the present form of this article. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NSF or the NIH.
PY - 2012
Y1 - 2012
N2 - The aim of this article is to develop a semiparametric model to describe the variability of the medial representation of subcortical structures, which belongs to a Riemannian manifold, and establish its association with covariates of interest, such as diagnostic status, age, and gender. We develop a two-stage estimation procedure to calculate the parameter estimates. The first stage is to calculate an intrinsic least squares estimator of the parameter vector using the annealing evolutionary stochastic approximation Monte Carlo algorithm, and then the second stage is to construct a set of estimating equations to obtain a more efficient estimate with the intrinsic least squares estimate as the starting point. We use Wald statistics to test linear hypotheses of unknown parameters and establish their limiting distributions. Simulation studies are used to evaluate the accuracy of our parameter estimates and the finite sample performance of theWald statistics.We apply our methods to the detection of the difference in the morphological changes of the left and right hippocampi between schizophrenia patients and healthy controls using a medial shape description. This article has online supplementary material.
AB - The aim of this article is to develop a semiparametric model to describe the variability of the medial representation of subcortical structures, which belongs to a Riemannian manifold, and establish its association with covariates of interest, such as diagnostic status, age, and gender. We develop a two-stage estimation procedure to calculate the parameter estimates. The first stage is to calculate an intrinsic least squares estimator of the parameter vector using the annealing evolutionary stochastic approximation Monte Carlo algorithm, and then the second stage is to construct a set of estimating equations to obtain a more efficient estimate with the intrinsic least squares estimate as the starting point. We use Wald statistics to test linear hypotheses of unknown parameters and establish their limiting distributions. Simulation studies are used to evaluate the accuracy of our parameter estimates and the finite sample performance of theWald statistics.We apply our methods to the detection of the difference in the morphological changes of the left and right hippocampi between schizophrenia patients and healthy controls using a medial shape description. This article has online supplementary material.
KW - Intrinsic least squares estimator
KW - Medial representation
KW - Semiparametric model
KW - Wald statistic
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U2 - 10.1080/01621459.2011.643710
DO - 10.1080/01621459.2011.643710
M3 - Article
C2 - 23794769
AN - SCOPUS:84862840997
SN - 0162-1459
VL - 107
SP - 12
EP - 23
JO - Journal of the American Statistical Association
JF - Journal of the American Statistical Association
IS - 497
ER -