\(M\)-type regression splines involving time series
DOI10.1016/S0378-3758(97)89714-5zbMath0879.62037OpenAlexW1991912642WikidataQ115438326 ScholiaQ115438326MaRDI QIDQ1360970
Publication date: 22 January 1998
Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/s0378-3758(97)89714-5
nonparametric regressionoptimal rate of convergenceregression splinestrictly stationary sequenceconditional mean functionconditional quantile functionbeta-mixing conditionconditional median function
Density estimation (62G07) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Asymptotic properties of nonparametric inference (62G20) Nonparametric robustness (62G35)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Density estimation in the \(L^ \infty\) norm for dependent data with applications to the Gibbs sampler
- Robust regression function estimation
- Uniform consistency of a class of regression function estimators
- Asymptotic distribution of robust estimators for nonparametric models from mixing processes
- Asymptotics for M-type smoothing splines
- Asymptotic integrated mean square error using least squares and bias minimizing splines
- Some mixing properties of time series models
- Additive regression and other nonparametric models
- Strong uniform convergence rates in robust nonparametric time series analysis and prediction: Kernel regression estimation from dependent observations
- Mixing properties of ARMA processes
- Robust nonparametric regression with simultaneous scale curve estimation
- Almost sure invariance principles for weakly dependent vector-valued random variables
- Multivariate adaptive regression splines
- Robust nonparametric regression in time series
- Nonparametric function estimation involving time series
- A practical guide to splines
- Central limit theorems for empirical and \(U\)-processes of stationary mixing sequences
- Rates of convergence for empirical processes of stationary mixing sequences
- Robust nonparametric regression estimation for dependent observations
- Optimal global rates of convergence for nonparametric regression
- \(M\)-type smoothing splines with auxiliary scale estimation
- [https://portal.mardi4nfdi.de/wiki/Publication:3038407 Propri�t�s de convergence presque compl�te du pr�dicteur � noyau]
- Convergence rate of b-spline estimators of nonparametric conditional quantile functions∗
- Uniform Consistency of Kernel Estimators of a Regression Function Under Generalized Conditions
- Nonparametric Density Estimation, Prediction, and Regression for Markov Sequences
- Flexible Parsimonious Smoothing and Additive Modeling
- Robust splines
- Regression Quantiles
- NEAREST‐NEIGHBOUR METHODS FOR TIME SERIES ANALYSIS
- Asymptotic behaviour of nonparametric conditional quantile estimates for time series
- Selection of dimension and basis for density estimation and selection of dimension, basis and error distribution for regression
- Convergence of stochastic processes
This page was built for publication: \(M\)-type regression splines involving time series
