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• W2149118818 abstract "Modelling dependence structure by means of long memory processes appears of great interest for many applications. This paper aims to show how nonparametric kernel estimators behave asymptotically under this dependence structure. We will consider a wide class of functional estimation problems, including distribution function, hazard function and density function, and we will give rates of weak consistency and of mean squared consistency. A main feature of this paper is to deal with a general form of long dependence processes, without assuming any Gaussian shape for the data or any parametric form for the covariances sequence. Finally, we will show how these results apply naturally to many classical parametric processes, and special attention will be paid to the Fractional Brownian Motion and to FARIMA processes." @default.
• W2149118818 created "2016-06-24" @default.
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• W2149118818 date "2003-08-01" @default.
• W2149118818 modified "2023-10-09" @default.
• W2149118818 title "Nonparametric estimation under long memory dependence" @default.
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• W2149118818 doi "https://doi.org/10.1080/10485250310001604668" @default.
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