Asymptotic results of error density estimator in nonlinear autoregressive models

DOI10.1007/s42952-024-00258-3MaRDI QIDQ6643290

Min Gao, Hongyan Fang, Wenzhi Yang, Shipeng Wu

Publication date: 26 November 2024

Published in: Journal of the Korean Statistical Society (Search for Journal in Brave)

zbMATH Keywords

asymptotic normalitykernel density estimatoruniform convergence ratenonlinear autoregressive models\( \alpha \)-mixing

Mathematics Subject Classification ID

Density estimation (62G07) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Asymptotic properties of nonparametric inference (62G20)

Cites Work

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• Asymptotics of kernel error density estimators in nonlinear autoregressive models
• Global property of error density estimation in nonlinear autoregressive time series models
• Strong consistency of the distribution estimator in the nonlinear autoregressive time series
• Nonparametric regression estimation under mixing conditions
• Nonparametric curve estimation from time series
• Asymptotic normality for density kernel estimators in discrete and continuous time
• Asymptotic normality of the kernel estimate of a probability density function under association
• Asymptotic normality of estimators in heteroscedastic errors-in-variables model
• Glivenko-Cantelli theorem for the kernel error distribution estimator in the first-order autoregressive model
• Two step estimations for a single-index varying-coefficient model with longitudinal data
• Nonlinear time series. Nonparametric and parametric methods
• Strong convergence of estimators in nonlinear autoregressive models
• Uniform strong consistency of kernel density estimators under dependence
• Oracally efficient estimation of autoregressive error distribution with simultaneous confidence band
• Parameter estimation for binomial \(\mathrm{AR}(1)\) models with applications in finance and industry
• A goodness-of-fit test of the errors in nonlinear autoregressive time series models
• Asymptotic normality of residual density estimator in stationary and explosive autoregressive models
• Estimation of the density and the regression function under mixing conditions.
• A goodness-of-fit test of the errors in nonlinear autoregressive time series models with stationary $\alpha$-mixing error terms
• Some probability inequalities of least-squares estimator in non linear regression model with strong mixing errors
• A CENTRAL LIMIT THEOREM AND A STRONG MIXING CONDITION
• Nonlinear Regression with Dependent Observations
• On a semiparametric regression model whose errors form a linear process with negatively associated innovations
• Asymptotic Properties of Error Density Estimator in Regression Model Under α-Mixing Assumptions
• Recursive probability density estimation for weakly dependent stationary processes
• Conditions for linear processes to be strong-mixing
• Asymptotic theory for partly linear models
• CONVERGENCE RATES OF ESTIMATORS IN PARTIAL LINEAR REGRESSION MODELS WITH MA(∞) ERROR PROCESS
• Threshold Autoregression with a Unit Root
• Law of the iterated logarithm for error density estimators in nonlinear autoregressive models
• Asymptotics of theL_p-Norms of Density Estimators in the Nonlinear Autoregressive Models

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