A semi-parametric approach to dual modeling when no replication exists

Abstract

In many applications, it is of interest to simultaneously model the mean and variance of a response when no replication exists. Modeling the mean and variance simultaneously is commonly referred to as dual modeling. Parametric approaches to dual modeling are popular when the underlying mean and variance functions can be expressed explicitly. Quite often, however, nonparametric approaches are more appropriate due to the presence of unusual curvature in the underlying functions. In sparse data situations, nonparametric methods often fit the data too closely while parametric estimates exhibit problems with bias. We propose a semi-parametric dual modeling approach [Dual Model Robust Regression (DMRR)] for non-replicated data. DMRR combines parametric and nonparametric fits resulting in improved mean and variance estimation. The methodology is illustrated with a data set from the literature as well as via a simulation study.