On sufficient conditions for the consistency of local linear kernel estimators

DOI10.1134/s0001434623090043OpenAlexW4387902273MaRDI QIDQ6084893

Unnamed Author

Publication date: 7 November 2023

Published in: Mathematical Notes (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1134/s0001434623090043

zbMATH Keywords

fixed designlocal linear estimatornonparametric regressionrandom designuniform consistencyhighly dependent design elements

Mathematics Subject Classification ID

Linear inference, regression (62Jxx) Inference from stochastic processes (62Mxx) Nonparametric inference (62Gxx)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Uniform convergence of estimator for nonparametric regression with dependent data
• Constructing initial estimators in one-step estimation procedures of nonlinear regression
• Strong consistency of the internal estimator of nonparametric regression with dependent data
• Estimation in semi-parametric regression with non-stationary regressors
• On internally corrected and symmetrized kernel estimators for nonparametric regression
• Some results on random design regression with long memory errors and predictors
• Uniform consistency of a class of regression function estimators
• Asymptotic distributions of nonparametric regression estimators for longitudinal or functional data
• Nonparametric regression estimation under mixing conditions
• Nonparametric estimation in a nonlinear cointegration type model
• Moment inequalities for sums of dependent random variables under projective conditions
• Estimation of the regression operator from functional fixed-design with correlated errors
• Strong uniform consistency of nonparametric regression function estimates
• Asymptotic properties of the multivariate Nadaraya-Watson regression function estimate: The fixed design case
• Nonparametric regression analysis of longitudinal data
• Nonparametric estimation of a regression function with dependent observations
• An interpolation method for adapting to sparse design in multivariate nonparametric regression
• Asymptotic normality of a wavelet estimator for asymptotically negatively associated errors
• A distribution-free theory of nonparametric regression
• Nonlinear time series. Nonparametric and parametric methods
• Nonparametric and semiparametric models.
• Universal weighted kernel-type estimators for some class of regression models
• Uniform convergence rates for a class of martingales with application in non-linear cointegrating regression
• Nonparametric regression estimation for dependent functional data: asymptotic normality
• Uniform in bandwidth consistency of kernel-type function estimators
• On the convergence rate of fixed design regression estimators for negatively associated random variables
• NONPARAMETRIC TRANSFORMATION REGRESSION WITH NONSTATIONARY DATA
• The choice of weights in kernel regression estimation
• Structural Nonparametric Cointegrating Regression
• Consistent nonparametric regression: Some generalizations in the fixed design case
• UNIFORM CONSISTENCY FOR NONPARAMETRIC ESTIMATORS IN NULL RECURRENT TIME SERIES
• UNIFORM CONVERGENCE RATES FOR KERNEL ESTIMATION WITH DEPENDENT DATA
• Weak and strong uniform consistency of kernel regression estimates
• The uniform convergence of the nadaraya‐watson regression function estimate
• Local Regression and Likelihood
• MULTIVARIATE LOCAL POLYNOMIAL REGRESSION FOR TIME SERIES:UNIFORM STRONG CONSISTENCY AND RATES
• Multivariate local polynomial regression for estimating average derivatives
• Local linear regression estimation under long-range dependence: strong consistency and rates
• Image Processing and Jump Regression Analysis
• Constructing Explicit Estimators in Nonlinear Regression Problems
• Asymptotic properties of one-step M-estimators
• Insensitivity of Nadaraya–Watson estimators to design correlation
• Trapezoidal rule and sampling designs for the nonparametric estimation of the regression function in models with correlated errors
• Remarks on Non-Parametric Estimates for Density Functions and Regression Curves
• Multivariate Local Polynomial Kernel Estimators: Leading Bias and Asymptotic Distribution
• Local polynomial estimation with a FARIMA-GARCH error process
• Towards Insensitivity of Nadaraya--Watson Estimators to Design Correlation

This page was built for publication: On sufficient conditions for the consistency of local linear kernel estimators