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Development And Application Of Bayesian Semiparametric Models For Dependent Data, Junshu Bao
Development And Application Of Bayesian Semiparametric Models For Dependent Data, Junshu Bao
Theses and Dissertations
Dependent data are very common in many research fields, such as medicine (repeated measures), finance (time series), traffic (clustered), etc. Effective control/modeling of the dependency among data can enhance the performance of the models and result in better prediction. In many cases, the correlation itself may be of great interest. In this dissertation, we develop novel Bayesian semi-/nonparametric regression models to analyze data with various dependence structures. In Chapter 2, a Bayesian non- parametric multivariate ordinal regression model is proposed to fit drinking behavior survey data from DWI offenders. The responses are two-dimensional ordinal data, drinking frequency and drinking quantity …
Semiparametric Regression Analysis Of Panel Count Data And Interval-Censored Failure Time Data, Bin Yao
Semiparametric Regression Analysis Of Panel Count Data And Interval-Censored Failure Time Data, Bin Yao
Theses and Dissertations
This dissertation discusses three important research topics on semiparametric regression analysis of panel count data and interval-censored data. Both types of data arise commonly in real-life studies in many fields such as epidemiology, social science, and medical research. In these studies, subjects are usually examined multiple times at periodical or irregular follow-up examinations. For panel count data, the response variable is the counts of some recurrent events, whose exact occurrence times are usually unknown. For interval-censored data, the response variable is the time to some events of interest, often called survival time or failure time, and the exact response time …