Asymptotic Properties of Error Density Estimator in Regression Model Under α-Mixing Assumptions

DOI10.1080/03610920802306741zbMath1162.62030OpenAlexW2057968384MaRDI QIDQ3631407

Publication date: 9 June 2009

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: http://www.informaworld.com/smpp/./content~db=all~content=a909856429

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Density estimation (62G07) Asymptotic properties of nonparametric inference (62G20) Strong limit theorems (60F15)

Related Items (2)

Asymptotic normality of Powell's kernel estimator ⋮ Nonparametric conditional variance and error density estimation in regression models with dependent errors and predictors

Cites Work

Unnamed Item
Unnamed Item
Unnamed Item
Asymptotic distributions of error density and distribution function estimators in nonparametric regression
A strong limit theorem for the oscillation modulus of the uniform empirical quantile process
Adaptive estimation of error density in nonparametric regression with small sample size
Asymptotic behavior of the empiric distribution of M-estimated residuals from a regression model with many parameters
The empirical distribution function of residuals from generalised regression
Kernel estimates under association: Strong uniform consistency
A weak convergence result useful in robust autoregression
Uniform strong estimation under \(\alpha\)-mixing, with rates
Mixing: Properties and examples
Weak convergence of randomly weighted dependent residual empiricals with applications to autoregression
Nonparametric smoothing and lack-of-fit tests
Estimating linear functionals of the error distribution in nonparametric regression
Weak and strong uniform consistency of a kernel error density estimator in nonparametric regression
Consistency of error density and distribution function estimators in nonparametric regression.
Empirical process of residuals for high-dimensional linear models
Asymptotics of some estimators and sequential residual empiricals in nonlinear time series
Estimation of the density of regression errors
Optimal nonparametric estimation of the density of regression errors with finite support
Non-parametric Estimation of the Residual Distribution
Strong limit theorems for oscillation moduli of the uniform empirical process
Convergence of Distributions Generated by Stationary Stochastic Processes

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