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Methods for functional regression an...

Chen, Yakuan.

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Methods for functional regression and nonlinear mixed-effects models with applications to PET data.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Methods for functional regression and nonlinear mixed-effects models with applications to PET data./
Author:	Chen, Yakuan.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
Description:	122 p.
Notes:	Source: Dissertation Abstracts International, Volume: 78-09(E), Section: B.
Contained By:	Dissertation Abstracts International78-09B(E).
Subject:	Biostatistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10269396
ISBN:	9781369704235

Methods for functional regression and nonlinear mixed-effects models with applications to PET data.
Chen, Yakuan.

Methods for functional regression and nonlinear mixed-effects models with applications to PET data. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 122 p.

Source: Dissertation Abstracts International, Volume: 78-09(E), Section: B.

Thesis (Ph.D.)--Columbia University, 2017.

The overall theme of this thesis focuses on methods for functional regression and nonlinear mixed-effects models with applications to PET data.

ISBN: 9781369704235Subjects--Topical Terms:

1002712
Biostatistics.

Methods for functional regression and nonlinear mixed-effects models with applications to PET data.
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100 1 $a Chen, Yakuan. $3 3290668
245 1 0 $a Methods for functional regression and nonlinear mixed-effects models with applications to PET data.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2017
300 $a 122 p.
500 $a Source: Dissertation Abstracts International, Volume: 78-09(E), Section: B.
500 $a Advisers: Todd Ogden; Jeff Goldsmith.
502 $a Thesis (Ph.D.)--Columbia University, 2017.
520 $a The overall theme of this thesis focuses on methods for functional regression and nonlinear mixed-effects models with applications to PET data.
520 $a The first part considers the problem of variable selection in regression models with functional responses and scalar predictors. We pose the function-on-scalar model as a multivariate regression problem and use group-MCP for variable selection. We account for residual covariance by "pre-whitening" using an estimate of the covariance matrix, and establish theoretical properties for the resulting estimator. We further develop an iterative algorithm that alternately updates the spline coefficients and covariance. Our method is illustrated by the application to two-dimensional planar reaching motions in a study of the effects of stroke severity on motor control.
520 $a The second part introduces a functional data analytic approach for the estimation of the IRF, which is necessary for describing the binding behavior of the radiotracer. Virtually all existing methods have three common aspects: summarizing the entire IRF with a single scalar measure; modeling each subject separately; and the imposition of parametric restrictions on the IRF. In contrast, we propose a functional data analytic approach that regards each subject's IRF as the basic analysis unit, models multiple subjects simultaneously, and estimates the IRF nonparametrically. We pose our model as a linear mixed effect model in which shrinkage and roughness penalties are incorporated to enforce identifiability and smoothness of the estimated curves, respectively, while monotonicity and non-negativity constraints impose biological information on estimates. We illustrate this approach by applying it to clinical PET data.
520 $a The third part discusses a nonlinear mixed-effects modeling approach for PET data analysis under the assumption of a compartment model. The traditional NLS estimators of the population parameters are applied in a two-stage analysis, which brings instability issue and neglects the variation in rate parameters. In contrast, we propose to estimate the rate parameters by fitting nonlinear mixed-effects (NLME) models, in which all the subjects are modeled simultaneously by allowing rate parameters to have random effects and population parameters can be estimated directly from the joint model. Simulations are conducted to compare the power of detecting group effect in both rate parameters and summarized measures of tests based on both NLS and NLME models. We apply our NLME approach to clinical PET data to illustrate the model building procedure.
590 $a School code: 0054.
650 4 $a Biostatistics. $3 1002712
690 $a 0308
710 2 $a Columbia University. $b Biostatistics. $3 2105522
773 0 $t Dissertation Abstracts International $g 78-09B(E).
790 $a 0054
791 $a Ph.D.
792 $a 2017
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10269396