 Strong uniform convergence rates in robust nonparametric time series analysis and prediction: Kernel regression estimation from dependent observationsGérard Collomb and Wolfgang Karl Härdle Stochastic Processes and their Applications, 1986, vol. 23, issue 1, pages 77-89 Abstract: Let be a strictly stationary real valued time series. We predict ZN + 1 from {Z1,...ZN} by a robust nonparametric method. The predictor is defined by the kernel method and constructed as a functional M-estimate connected with the conditional law of Zp+1 on Z1,...,Zp, when is Markovian of order p. Strong uniform convergence rates of this estimate are given together with some new results concerning robust regression kernel estimates from a sequence of valued, identically distributed and [phi]-mixing random pairs {(Xi, Yi); I = 1,...,n}. As a special case we obtain strong uniform convergence rates for estimators of the regression curve E(Y1/X1 = ·) and of the density of the law of X1. Keywords: robust; time; series; analysis; robust; prediction; robust; nonparametric; regression; M-estimation; rate; of; convergence; kernel; estimate; nonparametric; regression; and; density; estimation (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: http://EconPapers.repec.org/RePEc:eee:spapps:v:23:y:1986:i:1:p:77-89 Ordering information: This journal article can be ordered from Access Statistics for this article Stochastic Processes and their Applications is edited by T. Mikosch More articles in Stochastic Processes and their Applications from Elsevier |
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