Minimax wavelet estimation for multisample heteroscedastic nonparametric regression
DOI10.1080/10485252.2017.1406091zbMath1468.62270arXiv1511.04556OpenAlexW2963731938MaRDI QIDQ4634451
Madison Giacofci, Franck Picard, Sophie Lambert-Lacroix
Publication date: 10 April 2018
Published in: Journal of Nonparametric Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1511.04556
Besov classirregular dataminimax riskwavelet estimatoraverage then shrink approachfunctional mixed-effects modelsinhomogeneous Besov classmultisample datasetstochastically independent random function
Nonparametric regression and quantile regression (62G08) Density estimation (62G07) Applications of statistics to biology and medical sciences; meta analysis (62P10) Nontrigonometric harmonic analysis involving wavelets and other special systems (42C40) Generalized linear models (logistic models) (62J12)
Related Items (1)
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Lasso-type estimators for semiparametric nonlinear mixed-effects models estimation
- Adaptive variance function estimation in heteroscedastic nonparametric regression
- Block thresholding wavelet regression using SCAD penalty
- Estimation and inference in functional mixed-effects models
- Estimation of the mean of a multivariate normal distribution
- On the computation of wavelet coefficients
- Interpolation methods for nonlinear wavelet regression with irregularly spaced design
- Wavelets, approximation, and statistical applications
- On non-equally spaced wavelet regression
- Wavelet shrinkage for nonequispaced samples
- On minimax wavelet estimators
- Non-parametric Curve Estimation by Wavelet Thresholding with Locally Stationary Errors
- Adapting to Unknown Smoothness via Wavelet Shrinkage
- An insight into high-resolution mass-spectrometry data
- Wavelet-based Functional Mixed Models
- Bayesian Analysis of Mass Spectrometry Proteomic Data Using Wavelet-Based Functional Mixed Models
- Residual variance and residual pattern in nonlinear regression
- Ideal spatial adaptation by wavelet shrinkage
- On preconditioning the data for the wavelet transform when the sample size is not a power of two
- Regularization of Wavelet Approximations
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
- Wavelet‐Based Clustering for Mixed‐Effects Functional Models in High Dimension
This page was built for publication: Minimax wavelet estimation for multisample heteroscedastic nonparametric regression
