Asymptotic equivalence of nonparametric autoregression and nonparametric regression
Ion G. Grama, Michael H. Neumann
Abstract
It is proved that nonparametric autoregression is asymptotically equivalent in the sense of Le Cam's deficiency distance to nonparametric regression with random design as well as with regular nonrandom design.
Create a lesson
Related papers
Instance-Optimal Adaptive Location Estimation via Multiscale Mid-Summaries
Qiaosen Wang, Chao Gao
Robust Multi-Task Learning for Principal Component Analysis
Dali Liu, Haolei Weng
Principal component error in high-dimensional factor models
Alex Bernstein, Lisa R. Goldberg, Nicholas Gunther et al.
Approximation Theorems for High-Dimensional Canonical U-Statistics: Gaussian Chaos and Phase Transition
Leheng Cai, Qirui Hu
On the parametric and semiparametric Fisher information matrix for non-zero mean stationary spherical invariant random processes
Jean-Pierre Delmas, Habti Abeida, Stefano Fortunati
Inference for two-stage sampling in spatial surveys
Guillaume Chauvet, Olivier Bouriaud, Trinh H. K. Duong