Jfon parametric time series analysis and prediction: uniform almost sure convergence of the window and jt-nn autoregression estimates

DOI10.1080/02331888508801858zbMath0585.62162OpenAlexW2056814046MaRDI QIDQ3709721

Gérard Collomb

Publication date: 1985

Published in: Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02331888508801858

zbMATH Keywords

prediction errortime series analysiswindowstrictly stationary processuniform almost sure convergencephi-mixingautoregression functionk-nearest neighbour regression estimatesq-Markovian process

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Inference from stochastic processes and prediction (62M20) Nonparametric estimation (62G05) Non-Markovian processes: estimation (62M09)

Related Items (18)

Strong uniform convergence rates in robust nonparametric time series analysis and prediction: Kernel regression estimation from dependent observations ⋮ Consistent estimation of a general nonparametric regression function in time series ⋮ Nonparametric estimation of density derivatives of dependent data ⋮ Consistencia de un estimador no parametrico, recursivo, de la regresion bajo condiciones generales ⋮ Nonparametric inference for ergodic, stationary time series ⋮ k‐Nearest neighbors local linear regression for functional and missing data at random ⋮ Graph-based predictable feature analysis ⋮ Nonparametric regression estimation under mixing conditions ⋮ Nonparametric density and regression estimation for Markov sequences without mixing assumptions ⋮ Testing the functions defining a nonlinear autoregressive time series ⋮ Curve estimation for \(m_ n\)-decomposable time series including bilinear processes ⋮ Asymptotic distribution of data‐driven smoothers in density and regression estimation under dependence ⋮ Estimates of the tail of the stationary density function of certain nonlinear autoregressive processes ⋮ Uniform consistency in number of neighbors of the \(k\)NN estimator of the conditional quantile model ⋮ A local cross-validation algorithm for dependent data ⋮ On a partly linear autoregressive model with moving average errors ⋮ A statistical foundation for machine learning, with application to Go- Moku ⋮ Kernel autocorrelogram for time-deformed processes

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