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Regularized Local Smoothing for Longitudinal Analysis with Time-Varying Coefficient Models and Transformation Models.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Regularized Local Smoothing for Longitudinal Analysis with Time-Varying Coefficient Models and Transformation Models./
Author:	Ma, Xiaoyang.
Description:	1 online resource (106 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 84-03, Section: B.
Contained By:	Dissertations Abstracts International84-03B.
Subject:	Biostatistics. -
Online resource:	click for full text (PQDT)
ISBN:	9798845447869

Regularized Local Smoothing for Longitudinal Analysis with Time-Varying Coefficient Models and Transformation Models.
Ma, Xiaoyang.

Regularized Local Smoothing for Longitudinal Analysis with Time-Varying Coefficient Models and Transformation Models. - 1 online resource (106 pages)

Source: Dissertations Abstracts International, Volume: 84-03, Section: B.

Thesis (Ph.D.)--Georgetown University, 2022.

Includes bibliographical references

This dissertation concerns selecting locally influential variables in both conditional mean and conditional distribution-based models with high-dimensional longitudinal data. Motivated by a large epidemiological study for children and adolescent girls, we aim to search whether the risk factors for blood pressure change over age and find age-specific important covariates. To capture the dynamic covariate effects of the conditional mean-based models, we propose regularized kernel-based local polynomial smoothing time-varying coefficient models (TVCM) that can be used to select the locally influential covariates at the specific time range and estimate the local covariate effects. Our approach extends the local smoothing for TVCM to high-dimensional longitudinal data and is an alternative to the regularized spline methods studied by Wang et al. (2008) and Xue et al. (2020). To explore the temporal trends of outcome variable and covariates effect in conditional distribution-based models, we propose a two-step regularized smoothing time-varying transformation model. We adopt the iterative algorithm that combines the Majorize-Minimization (MM) algorithm for non-convex penalties with the Newton-Raphson algorithm. Through an application to the large epidemiological study, we demonstrate that our regularized local smoothing method has advantages over the regularized spline methods for being computationally simple and having straightforward clinical interpretations. Our simulation studies suggest that the proposed methods are capable of identifying locally influential predictors and can have consistent estimators of coefficients and conditional distribution functions at different time ranges. The proposed methods provide useful dynamic model-based tools for statistical machine learning with longitudinal data.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798845447869Subjects--Topical Terms:

783654
Biostatistics.
Subjects--Index Terms:

High-dimensional longitudinal dataIndex Terms--Genre/Form:

554714
Electronic books.

Regularized Local Smoothing for Longitudinal Analysis with Time-Varying Coefficient Models and Transformation Models.
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520 $a This dissertation concerns selecting locally influential variables in both conditional mean and conditional distribution-based models with high-dimensional longitudinal data. Motivated by a large epidemiological study for children and adolescent girls, we aim to search whether the risk factors for blood pressure change over age and find age-specific important covariates. To capture the dynamic covariate effects of the conditional mean-based models, we propose regularized kernel-based local polynomial smoothing time-varying coefficient models (TVCM) that can be used to select the locally influential covariates at the specific time range and estimate the local covariate effects. Our approach extends the local smoothing for TVCM to high-dimensional longitudinal data and is an alternative to the regularized spline methods studied by Wang et al. (2008) and Xue et al. (2020). To explore the temporal trends of outcome variable and covariates effect in conditional distribution-based models, we propose a two-step regularized smoothing time-varying transformation model. We adopt the iterative algorithm that combines the Majorize-Minimization (MM) algorithm for non-convex penalties with the Newton-Raphson algorithm. Through an application to the large epidemiological study, we demonstrate that our regularized local smoothing method has advantages over the regularized spline methods for being computationally simple and having straightforward clinical interpretations. Our simulation studies suggest that the proposed methods are capable of identifying locally influential predictors and can have consistent estimators of coefficients and conditional distribution functions at different time ranges. The proposed methods provide useful dynamic model-based tools for statistical machine learning with longitudinal data.
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653 $a Time-varying transformation model
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