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  • 标题:Adaptive confidence sets for kink estimation
  • 本地全文:下载
  • 作者:Viktor Bengs ; Hajo Holzmann
  • 期刊名称:Electronic Journal of Statistics
  • 印刷版ISSN:1935-7524
  • 出版年度:2019
  • 卷号:13
  • 期号:1
  • 页码:1523-1579
  • DOI:10.1214/19-EJS1555
  • 语种:English
  • 出版社:Institute of Mathematical Statistics
  • 摘要:We consider estimation of the location and the height of the jump in the $\gamma $-th derivative - a kink of order $\gamma $ - of a regression curve, which is assumed to be Hölder smooth of order $s\geq \gamma +1$ away from the kink. Optimal convergence rates as well as the joint asymptotic normal distribution of estimators based on the zero-crossing-time technique are established. Further, we construct joint as well as marginal asymptotic confidence sets for these parameters which are honest and adaptive with respect to the smoothness parameter $s$ over subsets of the Hölder classes. The finite-sample performance is investigated in a simulation study, and a real data illustration is given to a series of annual global surface temperatures.
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