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.
Age-Period-Cohort model, Smoothing method, Identifiability problem
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