Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/48344
Title: Bayesian Varying-Coefficient Model with Missing Data
Authors: HUANG ZHIPENG
Keywords: Baysian, Varying-coefficient, longitudinal data, missing data
Issue Date: 1-Aug-2013
Source: HUANG ZHIPENG (2013-08-01). Bayesian Varying-Coefficient Model with Missing Data. ScholarBank@NUS Repository.
Abstract: Motivated by Singapore Longitudinal Aging Study (SLAS), we propose a Bayesian approach for the estimation of semiparametric varying-coefficient models for longitudinal normal and cross-sectional binary responses. These models have proved to be more flexible than simple parametric regression models, and our Bayesian solution eases the computation complexity of these models. We also consider adapting all kinds of familiar statistical strategies to address the missing data issue in SLAS. Our simulation results indicate that Bayesian imputation approach performs better than complete-case and available-case approaches, especially under small sample designs, and may provide more useful results in practice. In the real data analysis for SLAS, the results from Bayesian imputation are similar to available-case analysis, differing from those with complete-case analysis.
URI: http://scholarbank.nus.edu.sg/handle/10635/48344
Appears in Collections:Ph.D Theses (Open)

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
HuangZP.pdf2.27 MBAdobe PDF

OPEN

NoneView/Download

Page view(s)

157
checked on Dec 18, 2017

Download(s)

245
checked on Dec 18, 2017

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.