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文章基本信息

  • 标题:Double Kernel Method Using Line Transect Sampling
  • 本地全文:下载
  • 作者:Omar Eidous ; M. K. Shakhatreh
  • 期刊名称:Austrian Journal of Statistics
  • 出版年度:2012
  • 卷号:41
  • 期号:02
  • 出版社:Austrian Statistical Society
  • 摘要:

    A double kernel method as an alternative to the classical kernel
    method is proposed to estimate the population abundance by using line transect
    sampling. The proposed method produces an estimator that is essentially
    a kernel type of estimator use the kernel estimator twice to improve the performances
    of the classical kernel estimator. The feasibility of using bootstrap
    techniques to estimate the bias and variance of the proposed estimator is also
    addressed. Some numerical examples based on simulated and real data are
    presented. The results show that the proposed estimator outperforms existing
    classical kernel estimator in most considered cases.

  • 关键词:Line Transect Sampling; Kernel Methods; Boundary Effect; Reflection Method
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