A Smoothing Model and Its Asymptotics with Applications to Health Studies and Social Research

Smoothing is a data-driven technique in statistical modeling. It has many desirable properties, and can be applied to modeling complex data. In this dissertation, a smoothing cohort model is considered as an effective alternative to address the identifiability problem in age-period-cohort analysis, in which multiple estimators are induced by a linear dependence of covariates: Period - Age = Cohort in the regression model of APC analysis. The smoothing cohort model yields consistent estimation of age and period effects, but cohort effect estimation is biased. Hence, the second stage model aims to correct the bias by setting a constraint using the consistent estimation of age or period effect from the first stage. Selection of constraints in the second stage is studied through simulations. The large sample behavior of the model parameter estimation is examined. The method is applied to cancer-incidence rate, mortality rate, and homicide-arrest rate data and yields sensible trend estimation in age, period, and cohort.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

Doctoral

Discipline thesis:degree_discipline

Mathematics

Grantor

University of Houston

Year dc:date.issued

2016

Author dc:creator

• Huang, Shujiao 1989-

Advisor dc:contributor.advisor

• Fu, Wenjiang

Committee members dc:contributor.committeemember

• Azencott, Robert
• Papadakis, Emanuel I.
• Yang, Yipeng

• Age-Period-Cohort model
• Smoothing method
• Identifiability problem

Statement dc:rights

• The author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/10657/5422

OAI identifier oai:identifier

oai:uh-ir.tdl.org:10657/5422