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Zbl 0764.62073
Roussas, George G.; Tran, Lanh T.; Ioannides, D.A.
Fixed design regression for time series: Asymptotic normality. (English)
[J] J. Multivariate Anal. 40, No.2, 262-291 (1992). ISSN 0047-259X

The authors study nonparametric regression models with dependent errors. More specifically, it is assumed that the error variables form a stationary strongly mixing process. Kernel type smoothers are studied as estimators of the regression function, including as examples the Gasser- Müller and Priestley-Chao estimators. Asymptotic normality of these estimators is established under various assumptions on the mixing rates and on the weights.
[H.Dehling (Groningen)]

MSC 2000:: *62M10 Time series, etc. (statistics)
62E20 Asymptotic distribution theory in statistics
62G07 Curve estimation
62G20 Nonparametric asymptotic efficiency

Keywords: time series; general weights; Gasser-Müller estimate; autoregressive processes; fixed design; strict stationarity; bounded case; unbounded case; kernel type smoothers; asymptotic normality; nonparametric regression models; dependent errors; stationary strongly mixing process; Priestley-Chao estimators

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