Mazimum Likelihood and Kernel Estimate Methods for Line Transect Density Estimation

摘要：

The probability density function at perpendicular distance zero from data obtained by the line transects method. A nonparametric kernel method produces asymptotic unbiased estimator for f (0) provided that the detection model has a shoulder at distance zero, that is, f (0) = o ? If the shoulder condition seems to be valid using as reference the half normal density, while if the shoulder condition does not seem to be valid, use the exponential density as a reference. The results demonstrate the superiority of the better estimator in most cases considered because without the shoulder condition, kernel estimate is not as good as maximum likelihood, and maximum likelihood estimate is quite effective. But, kernel estimate after improving is better than maximum likelihood estimate for line transect sampling.

关键词： Mazimum Likelihood Kernel Estimate Line Transect RRMSE

作者: XIONG Guojing

作者单位: School of Economy and Management, Nanchang University, P.R.China, 330031

会议类型: 国际会议

会议名称: 2009 International Institute of Applied Statistics Studies(2009 国际应用统计学术研讨会)

会议地点: 青岛

会议语种:英文

页码: 1-4

在线出版日期: 2009-07-25（万方平台首次上网日期，不代表论文的发表时间）