Title: A Mallows-type model averaging estimator for the varying-coefficient partially linear model
Authors: Rong Zhu - Academy of Mathematics and Systems Science, Chinese Academy of Sciences (China) [presenting]
Alan Wan - City University of Hong Kong (Hong Kong)
Xinyu Zhang - Academy of Mathematics and Systems Science, Chinese Academy of Sciences (China)
Guohua Zou - Capital Normal University (China)
Abstract: In the last decade, significant theoretical advances have been made in the area of frequentist model averaging (FMA); however, the majority of this work has emphasised parametric model setups. FMA for the semiparametric varying-coefficient partially linear model (VCPLM) is considered. VCPLM has gained prominence to become an extensively used modeling tool in recent years. Within this context, we develop a Mallows-type criterion for assigning model weights and prove its asymptotic optimality. A simulation study and a real data analysis demonstrate that the FMA estimator that arises from this criterion is vastly preferred to estimators obtained by information criterion score-based model selection and FMA methods. The analysis is complicated by the fact that for the VCPLM, uncertainty is not only with respect to the choice of covariates, but also to the component in the model to which the covariate belongs.