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  • 标题:Density estimation for $\tilde{\beta}$-dependent sequences
  • 本地全文:下载
  • 作者:Jérôme Dedecker ; Florence Merlevède
  • 期刊名称:Electronic Journal of Statistics
  • 印刷版ISSN:1935-7524
  • 出版年度:2017
  • 卷号:11
  • 期号:1
  • 页码:981-1021
  • DOI:10.1214/17-EJS1249
  • 语种:English
  • 出版社:Institute of Mathematical Statistics
  • 摘要:We study the ${\mathbb{L}}^{p}$-integrated risk of some classical estimators of the density, when the observations are drawn from a strictly stationary sequence. The results apply to a large class of sequences, which can be non-mixing in the sense of Rosenblatt and long-range dependent. The main probabilistic tool is a new Rosenthal-type inequality for partial sums of $BV$ functions of the variables. As an application, we give the rates of convergence of regular Histograms, when estimating the invariant density of a class of expanding maps of the unit interval with a neutral fixed point at zero. These Histograms are plotted in the section devoted to the simulations.
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