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  • 标题:Adaptive confidence intervals for the tail coefficient in a wide second order class of Pareto models
  • 本地全文:下载
  • 作者:Alexandra Carpentier ; Arlene K. H. Kim
  • 期刊名称:Electronic Journal of Statistics
  • 印刷版ISSN:1935-7524
  • 出版年度:2014
  • 卷号:8
  • 期号:2
  • 页码:2066-2110
  • DOI:10.1214/14-EJS944
  • 语种:English
  • 出版社:Institute of Mathematical Statistics
  • 摘要:We study the problem of constructing uniform and adaptive confidence intervals for the tail coefficient in a second order Pareto model, when the second order coefficient is unknown. This problem is translated into a testing problem on the second order parameter. By constructing an appropriate model and an associated test statistic, we provide a uniform and adaptive confidence interval for the first order parameter. We also provide an almost matching lower bound, which proves that the result is minimax optimal up to a logarithmic factor.
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