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测线调查样本的核函数密度推定中探知函数改良

Comparative Study of Maximum Likelihood and Kernel Estimations for Density Estimation in Line Transect Sampling
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摘要 考虑用最大似然法和核函数法对于测线调查的密度推定,选用4个核函数、3个最佳带幅宽;分别用2个探知函数生成计算机模拟测线调查数据,对两种密度推定法进行比较研究. We consider the estimation of biological population density based on line transect data. Maximum likelihood estimate and nonparametric kernel method for the different bandwidths of optimal smoothing parameter and detection functions is employed.A Comparison between two density estinating methods are made and discussed.
作者 熊国经 许青
出处 《南昌工程学院学报》 CAS 2005年第2期25-28,45,共5页 Journal of Nanchang Institute of Technology
基金 日本文部科学省国家科学费专项资助研究课题(11304003)
关键词 探知函数 核函数 最大似然估计 相对根平均平方误差 DF kernel maximum likelihood estimate RRMSE
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参考文献8

  • 1Chen S X. A kernel Estimate for the Density of a Biological Population by Using Line Transect Sampling[J]. Appl Statist,1996,45(2): 135-150.
  • 2N Altman, C Leger. Bandwidth Selection for Kernel Distribution Function Estimation[J]. Journal of Statistical Planning and Inference,1995,46:195-214.
  • 3Sliverman B W. Density Estimation[M]. Chapman and Hall,1986.
  • 4W R Schucany. Local Bandwidth Selection for Kernel Estimation of Population Densities with Line Transect Sampling[J]. Biometrics, 1999(55):769-773.
  • 5Xiong Guojing,T Tarumi.On Density Estimation for Kernel Function and Bandwidth by Using Line Transect Sampling[C], Proceeding of the 17th Symposium of the JSCS,2003:135-138.
  • 6Y P Mack, P X Quang, S Zhang. Kernel Estimation in Transect Sampling Without the Shoulder Condition[J]. Communications in Statistics Theory and Methods,1999, 28(10): 2277-2296.
  • 7Zhang S. Improvements on the Kernel Estimation in Line Transect Sampling Without the Shoulder Condition[J]. Statistics & Probability Letters,2001, 53: 249-258.
  • 8熊国経.ライントランセクトサンプリングに对する密度推定についての研究[D],日本国会图书馆,2004.

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