title: Parametrically guided estimation in nonparametric varying coefficient models with quasi-likelihood.
abstract: Varying coefficient models allow us to generalize standard linear regression models to incorporate complex covariate effects by modeling the regression coefficients as functions of another covariate. For nonparametric varying coefficients, we can borrow the idea of parametrically guided estimation to improve asymptotic bias. In this paper, we develop a guided estimation procedure for the nonparametric varying coefficient models. Asymptotic properties are established for the guided estimators and a method of bandwidth selection via bias-variance tradeoff is proposed. We compare the performance of the guided estimator with that of the unguided estimator via both simulation and real data examples.
authors.affiliation: Department of Statistics, North Carolina State University, Raleigh, NC 27695, U.S.A.
authors.forename: Clemontina A
authors.initials: CA
authors.lastname: Davenport
authors.affiliation: Department of Statistics, North Carolina State University, Raleigh, NC 27695, U.S.A.
authors.forename: Arnab
authors.initials: A
authors.lastname: Maity
authors.affiliation: Department of Statistics, North Carolina State University, Raleigh, NC 27695, U.S.A.
authors.forename: Yichao
authors.initials: Y
authors.lastname: Wu
keywords: generalized linear models; local polynomial smoothing; nonparametric regression; parametrically guided estimation; varying coefficient model