Citation
Kunihama, T. (2015). Nonparametric Bayes Analysis of Social Science Data.
Abstract
Social science data often contain complex characteristics that standard statistical methods fail to capture. Social surveys assign many questions to respondents, which often consist of mixed-scale variables. Each of the variables can follow a complex distribution outside parametric families and associations among variables may have more complicated structures than standard linear dependence. Therefore, it is not straightforward to develop a statistical model which can approximate structures well in the social science data. In addition, many social surveys have collected data over time and therefore we need to incorporate dynamic dependence into the models. Also, it is standard to observe massive number of missing values in the social science data. To address these challenging problems, this thesis develops exible nonparametric Bayesian methods for the analysis of social science data. Chapter 1 briefy explains backgrounds and motivations of the projects in the following chapters. Chapter 2 develops a nonparametric Bayesian modeling of temporal dependence in large sparse contingency tables, relying on a probabilistic factorization of the joint pmf. Chapter 3 proposes nonparametric Bayes inference on conditional independence with conditional mutual information used as a measure of the strength of conditional dependence. Chapter 4 proposes a novel Bayesian density estimation method in social surveys with complex designs where there is a gap between sample and population. We correct for the bias by adjusting mixture weights in Bayesian mixture models. Chapter 5 develops a nonparametric model for mixed-scale longi-tudinal surveys, in which various types of variables can be induced through latent continuous variables and dynamic latent factors lead to exibly time-varying associations among variables.
URL
https://stat.duke.edu/sites/default/files/people/theses/TsuyoshiKunihamaDissertation.pdfReference Type
Thesis/Dissertation
Book Title
Statistical Science
Author(s)
Kunihama, T.
Year Published
2015
Publisher
Duke University
Reference ID
5761
