Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/153729
Title: MODEL AVERAGING FOR LONGITUDINAL COVARIANCE ESTIMATION AND BAYESIAN NONPARAMETRIC RGRESSION
Authors: WANG JINGLI
Keywords: Model averaging, GEE, nonparametric regression
Issue Date: 21-Jan-2019
Citation: WANG JINGLI (2019-01-21). MODEL AVERAGING FOR LONGITUDINAL COVARIANCE ESTIMATION AND BAYESIAN NONPARAMETRIC RGRESSION. ScholarBank@NUS Repository.
Abstract: In this thesis, we propose two model averaging methods for the correlation structure of GEE on longitudinal data and Bayesian nonparametric regression. Under the GEE framework we use a weighted sum of a group of patterned correlation matrices to estimate the correlation. Consequently, a consistent and efficient estimator of the correlation structure can be found. For Bayesian nonparametric regression, we present a model averaging method to construct a prediction function in semiparametric form. The weighted sum of candidate nonparametric models is taken as a predictor of mean response. Marginal nonparametric regression models are approximated by spline basis functions. The optimal model weights are estimated by minimizing the least squares criterion with an explicit form. This method is demonstrated to be more accurate than both classical parametric model averaging methods and existing semiparametric regression models. We implement our methods in extensive simulation studies and illustrate them with some real data examples.    
URI: https://scholarbank.nus.edu.sg/handle/10635/153729
Appears in Collections:Ph.D Theses (Open)

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