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Regularized Local Smoothing for Long...

Ma, Xiaoyang.

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

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Regularized Local Smoothing for Longitudinal Analysis with Time-Varying Coefficient Models and Transformation Models./
作者:	Ma, Xiaoyang.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2022,
面頁冊數:	106 p.
附註:	Source: Dissertations Abstracts International, Volume: 84-03, Section: B.
Contained By:	Dissertations Abstracts International84-03B.
標題:	Biostatistics. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29327741
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. - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 106 p.

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

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

This item must not be sold to any third party vendors.

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.

ISBN: 9798845447869Subjects--Topical Terms:

1002712
Biostatistics.
Subjects--Index Terms:

High-dimensional longitudinal data

Regularized Local Smoothing for Longitudinal Analysis with Time-Varying Coefficient Models and Transformation Models.
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500 $a Source: Dissertations Abstracts International, Volume: 84-03, Section: B.
500 $a Advisor: Wu, Colin;Tan, Ming.
502 $a Thesis (Ph.D.)--Georgetown University, 2022.
506 $a This item must not be sold to any third party vendors.
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 Regularized smoothing
653 $a Statistical machine learning
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653 $a Time-varying transformation model
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