Robust nonparametric equivariant M-estimators for the regression function have been extensively studied when the covariates are in Rk. In this paper, we derive strong uniform convergence rates for kernel-based robust equivariant M-regression estimator when the covariates are functional.

We consider robust testing on the regression parameter of a partially linear regression model, where missing responses are allowed. We derive the asymptotic behavior of the proposed test statistic under the null and contiguous alternatives. A numerical study is performed.

In this paper, we introduce a family of robust statistics which allow to decide between a parametric model and a semiparametric one. More precisely, under a generalized partially linear model, i.e., when the observations satisfy yi|(xi,ti)∼F(⋅,μi) with μi=H(η(ti)+xit β) and H a known...

This paper analyzes the higher-order asymptotic properties of generalized method of moments (GMM) estimators for linear time series models using many lags as instruments. A data-dependent moment selection method based on minimizing the approximate mean squared error is developed. In addition, a...