Functional Regression Model with Autocorrelation: Applications to Cancer Mortality Rates

Music City Center 

This report extends the Generalized Least Squares (GLS) method to accommodate functional regression models with dependent errors. Specifically, we apply an AR(1) autocorrelation structure to effectively model and forecast age-adjusted lung cancer mortality rates across nine U.S. registries. Utilizing data recorded from 1975 to 2015 for various age groups, we investigate the intrinsic functional structure of these mortality rates. Our study further evaluates the predictive performance of the functional regression model in comparison to classical time series methods, such as ARIMA.

Functional Data

Autocorrelation

ARIMA

Registries

Time Series

Regression 

Section on Statistics in Epidemiology