作者：Studer, M; Seifert, B; Gasser, T
作者单位：University of Zurich
摘要：Due to the curse of dimensionality, estimation in a multidimensional nonparametric regression model is in general not feasible. Hence, additional restrictions are introduced, and the additive model takes a prominent place. The restrictions imposed can lead to serious bias. Here, a new estimator is proposed which allows penalizing the nonadditive part of a regression function. This offers a smooth choice between the full and the additive model. As a byproduct, this penalty leads to a regulariza...
作者：Bhattacharya, R; Patrangenaru, V
作者单位：University of Arizona; Texas Tech University System; Texas Tech University
摘要：This article develops nonparametric inference procedures for estimation and testing problems for means on manifolds. A central limit theorem for Frechet sample means is derived leading to an asymptotic distribution theory of intrinsic sample means on Riemannian manifolds. Central limit theorems are also obtained for extrinsic sample means w.r.t. an arbitrary embedding of a differentiable manifold in a Euclidean space. Bootstrap methods particularly suitable for these problems are presented. Ap...
作者：He, XM
作者单位：University of Illinois System; University of Illinois Urbana-Champaign
