Asymptotically Sufficient Statistics in Nonparametric Regression Experiments with Correlated Noise
We find asymptotically sufficient statistics that could help simplify inference in nonparametric regression problems with correlated errors. These statistics are derived from a wavelet decomposition that is used to whiten the noise process and to effectively separate high-resolution and low-resoluti...
Saved in:
Main Author: | Andrew V. Carter |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2009-01-01
|
Series: | Journal of Probability and Statistics |
Online Access: | http://dx.doi.org/10.1155/2009/275308 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Multi-Task Nonparametric Regression Under Joint Sparsity
by: Jae-Hwan Jhong, et al.
Published: (2025-01-01) -
Sufficient statistics for the Pareto distribution parameter
by: I. S. Pulkin, et al.
Published: (2021-06-01) -
Combination Estimation of Smoothing Spline and Fourier Series in Nonparametric Regression
by: Ni Putu Ayu Mirah Mariati, et al.
Published: (2020-01-01) -
A New Mixed Estimator in Nonparametric Regression for Longitudinal Data
by: Made Ayu Dwi Octavanny, et al.
Published: (2021-01-01) -
Nonparametric Regression with Subfractional Brownian Motion via Malliavin Calculus
by: Yuquan Cang, et al.
Published: (2014-01-01)