The marked empirical process to test nonlinear time series against a large class of alternatives when the random vectors are nonstationary and absolutely regular

DOI10.1080/02331888.2010.507406zbMath1314.62204OpenAlexW2004142664MaRDI QIDQ2892897

Michel Harel, Echarif Elharfaoui

Publication date: 25 June 2012

Published in: Unnamed Author (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02331888.2010.507406

zbMATH Keywords

nonstationarityresidualsmarked empirical processSkorohod topologymodel check for regressiongeneral AR modelgeneral AR-ARCH modelgeometrical absolute regularity

Mathematics Subject Classification ID

Nonparametric hypothesis testing (62G10) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Asymptotic properties of nonparametric inference (62G20) Central limit and other weak theorems (60F05) Order statistics; empirical distribution functions (62G30) Functional limit theorems; invariance principles (60F17)

Related Items (1)

Testing nonstationary and absolutely regular nonlinear time series models

Cites Work

• Unnamed Item
• Checking nonlinear heteroscedastic time series models
• Asymptotic normality of nearest neighbor regression function estimates
• Kernel regression estimation for random fields
• Conditional empirical processes defined by \(\Phi\)-mixing sequences
• Local power of a Cramér-von Mises type test for parametric autoregressive models of order one
• On almost sure convergence of conditional empirical distribution functions
• Autoregression quantiles and related rank score processes for generalized random coefficient autoregressive processes.
• Conditional empirical processes defined by nonstationary absolutely regular sequences
• Nonparametric model checks for regression
• Nonparametric model checks for time series
• Nonparametric density estimators based on nonstationary absolutely regular random sequences
• The mixing property of bilinear and generalised random coefficient autoregressive models
• Goodness-of-fit tests for nonlinear heteroscedastic regression models
• Semiparametrically efficient rank-based inference for shape. I: optimal rank-based tests for sphericity
• Semiparametrically efficient rank-based inference for shape. II: Optimal \(R\)-estimation of shape
• The space \(\tilde D_ k\) land weak convergence for the rectangle-indexed processes under mixing
• A Smooth Test of Goodness-of-Fit for Growth Curves and Monotonic Nonlinear Regression Models
• ON TESTING THE GOODNESS-OF-FIT OF NONLINEAR HETEROSCEDASTIC REGRESSION MODELS
• Weak convergence of some marked empirical processes: Application to testing heteroscedasticity

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