Additive regression model for stationary and ergodic continuous time processes
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Publication:2979007
DOI10.1080/03610926.2015.1048882zbMath1380.62165OpenAlexW2315257730MaRDI QIDQ2979007
Publication date: 2 May 2017
Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/03610926.2015.1048882
consistencynormalityergodicitymartingale differencesstationarityregression functionadditive modelkernel-type estimatorsmarginal integration
Nonparametric regression and quantile regression (62G08) Density estimation (62G07) Asymptotic properties of nonparametric inference (62G20) Exchangeability for stochastic processes (60G09)
Related Items (5)
Some Characteristics of the Conditional Set-Indexed Empirical Process Involving Functional Ergodic Data ⋮ Some results about kernel estimators for function derivatives based on stationary and ergodic continuous time processes with applications ⋮ Some asymptotic properties of kernel regression estimators of the mode for stationary and ergodic continuous time processes ⋮ Uniform convergence rate of the kernel regression estimator adaptive to intrinsic dimension in presence of censored data ⋮ Asymptotics for function derivatives estimators based on stationary and ergodic discrete time processes
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