CONVERGENCE RATES OF ESTIMATORS IN PARTIAL LINEAR REGRESSION MODELS WITH MA(∞) ERROR PROCESS
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Publication:4449097
DOI10.1081/STA-120017224zbMath1075.62550MaRDI QIDQ4449097
Xian Zhou, Xiaoqian Sun, Gemai Chen, Jin-hong You
Publication date: 4 February 2004
Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)
Linear time series errorsNonparametric kernel smoothingPartial linear regression modelSemiparametric least squares estimatorStrong convergence rates
Nonparametric regression and quantile regression (62G08) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Asymptotic properties of nonparametric inference (62G20)
Related Items (13)
Empirical Likelihood for a Heteroscedastic Partial Linear Errors-in-Variables Model ⋮ Asymptotic Normality of Estimators in Heteroscedastic Semi-Parametric Model with Strong Mixing Errors ⋮ Empirical likelihood based estimation for a class of functional coefficient ARCH-M models ⋮ Strong consistency of estimators in partially linear models for longitudinal data with mixing-dependent structure ⋮ Berry-Esseen bounds for wavelet estimator in semiparametric regression model with linear process errors ⋮ CLT of wavelet estimator in semiparametric model with correlated errors ⋮ Semiparametric generalized least squares estimation in partially linear regression models with correlated errors ⋮ A general result on complete convergence for weighted sums of linear processes and its statistical applications ⋮ Berry-Esseen type bounds of estimators in a semiparametric model with linear process errors ⋮ Empirical likelihood inference for semiparametric model with linear process errors ⋮ Asymptotic normality in partial linear models based on dependent errors ⋮ Berry-Esseen type bounds of the estimators in a semiparametric model under linear process errors with α-mixing dependent innovations ⋮ On a semiparametric regression model whose errors form a linear process with negatively associated innovations
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