Statistics and Data Science Seminar
Soo-Young Kim
Fred Hutchinson Cancer Center
Improved inference in heteroskedastic regression models with monotone variance function estimation
Abstract: Various methods for estimating variance functions in heteroscedastic regression models have been developed over the years. We propose methods to estimate a variance function in a heteroskedastic regression model where the variance function is assumed to be smooth and monotone in a predictor variable. The estimation method is based on the maximum likelihood principle, and its computation is carried out through regression splines and the cone projection algorithm. The convergence rate of the estimated variance function is derived, and simulations show that it tends to be closer to the true variance function in a variety of scenarios compared to the existing methods. The estimated variance function from the proposed method provides improved inference about the mean function, in terms of a coverage probability and an average length for an interval estimate. The utility of the method is illustrated through the analysis of real datasets.
Wednesday November 2, 2022 at 4:00 PM in Zoom