Spring Seminar Series - April 20, 2006
University of Minnesota
School of Statistics
College of Liberal Arts
Nonparametric
Transformation Regression Models for Skewed Data with Heteroscedastic
Variance
Xiao-Hua
Andrew Zhou
Department of Biostatistics
University of Washington
Thursday, April 20, 2006
3:30 PM, 115
Ford Hall
Minneapolis, East Bank Campus
Social at 3:00 PM, 300 Ford Hall
Abstract
In
this talk we introduce a new non-parametric heteroscedastic
Transformation regression model that allows us to predict the
expected value of an outcome of a patient with given covariates
when the distribution of the outcome is highly skewed with a
heteroscedastic variance. In our new model, we allow both the
transformation function and the error distribution function to be
unknown. We show that estimators for regression parameters, the
expected value of the original outcome, and the transformation
function converge to their true values at the same rate as that one can
expect for a parametric model. In a simulation study, we demonstrate
that our proposed nonparametric method is robust with little loss of
efficiency. Finally, we apply our new model to a study on health care
costs. This is a joint work with Huazhen Lin.