Joint asymptotics for semi-nonparametric regression models with partially linear structure

Abstract

We consider a joint asymptotic framework for studying semi-nonparametric regression models where (finite-dimensional) Euclidean parameters and (infinite-dimensional) functional parameters are both of interest. The class of models in consideration share a partially linear structure and are estimated in two general contexts: (i) quasi-likelihood and (ii) true likelihood. We first show that the Euclidean estimator and (pointwise) functional estimator, which are re-scaled at different rates, jointly converge to a zero-mean Gaussian vector. This weak convergence result reveals a surprising joint asymptotics phenomenon: these two estimators are asymptotically independent. A major goal of this paper is to gain first-hand insights into the above phenomenon. Moreover, a likelihood ratio testing is proposed for a set of joint local hypotheses, where a new version of the Wilks phenomenon [Ann. Math. Stat. 9 (1938) 60-62; Ann. Statist. 1 (2001) 153-193] is unveiled. A novel technical tool, called a joint Bahadur representation, is developed for studying these joint asymptotics results.

Published at http://dx.doi.org/10.1214/15-AOS1313 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)

References in corpus (4)

• Nonparametric inference in generalized functional linear models
• Local and global asymptotic inference in smoothing spline models
• General frequentist properties of the posterior profile distribution
• Mixed-rates asymptotics

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