Asymptotics of the \(L_p\)-norms of density estimators in the first-order autoregressive models.

DOI10.1016/j.spl.2003.06.006zbMath1116.62340OpenAlexW2050922990MaRDI QIDQ5967098

Lajos Horváth, Ričardas Zitikis

Publication date: 14 February 2004

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.spl.2003.06.006

zbMATH Keywords

AR(1) processesStationary processesResiduals\(L_p\)-norms

Mathematics Subject Classification ID

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

Related Items (4)

Glivenko-Cantelli theorem for the kernel error distribution estimator in the first-order autoregressive model ⋮ Global property of error density estimation in nonlinear autoregressive time series models ⋮ Empirical likelihood inference for error density estimators in first-order autoregression models ⋮ Asymptotics of theL_p-Norms of Density Estimators in the Nonlinear Autoregressive Models

Cites Work

• Weak convergence of the residual empirical process in explosive autoregression
• Central limit theorems for \(L_ p\)-norms of density estimators
• Weighted empirical processes in dynamic nonlinear models.
• On the Bickel-Rosenblatt test for first-order autoregressive models
• On the asymptotic normality of \(L_ p\)-norms of empirical functionals
• On some global measures of the deviations of density function estimates
• On Testing Hypotheses in the Sliding Average Scheme by the Kolmogorov–Smirnov and $\omega ^2 $ Tests
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