On Improving Kernel Estimators Using Line Transect Sampling

2010

Environmetrics

The variable location kernel (VLK) method provides a nonparametric estimator for a probability density function. This article proposes the VLK method to fit line transect data in order to estimate the density of a biological population. The method produces two promising estimators for the density of objects which improve upon the performance of the classical kernel estimator. Although the two proposed estimators share a common form, they exhibit rather different performances. To compute the bias and variance of the proposed estimators, the bootstrap technique is proposed. For a wide range of possible models for line transect data, a comparison of the two estimators and the classical kernel estimator is carried out by simulation. The results show the practical potential of the proposed estimators over the classical kernel estimator for almost all cases considered. Two previously published data sets are also analyzed and the results confirm the good performances of the proposed estimators.

Section: Simulation Design and Resultssupporting

confidence: 88%

Section: Introductionmentioning

confidence: 96%

See 1 more Smart Citation

Variable location kernel method using line transect sampling

2010

Environmetrics

“…Mack (2002) considered some methods of bias correction when the kernel method is used in constructing confidence intervals for wildlife abundance based on transect data. Eidous (2005a) proposed some methods to improve the performance of the kernel estimator using line transect data. He reduced the bias of the kernel estimator from O h 2 to O h 3 as h → 0 under the shoulder condition assumption.…”

Section: Introductionmentioning

confidence: 99%

A Semiparametric Model for Line Transect Sampling

Communications in Statistics - Theory and Methods

2006

This paper introduces an appealing semiparametric model for estimating wildlife abundance based on line transect data. The proposed method requires the existence of a parametric model and then improves the estimator using a kernel method. Properties of the resultant estimator are derived and an expression for the asymptotic mean square error (AMSE) of the estimator is given. Minimization of the AMSE leads to an explicit formula for an optimal choice of the smoothing parameter. Small-sample properties of the proposed estimator using the parametric half-normal model are investigated and compared with the classical kernel estimator using both simulations and real data. Numerical results show that improvements over the classical kernel estimator often can be realized even when the true density is far from the half-normal model.

“…Recent work has focused on employing the nonparametric classical kernel method to estimate f (0). Some initial efforts in applying the kernel method to line transect data were made by Buckland (1992), Chen (1996), Mack and Quang (1998) and Eidous (2005Eidous ( , 2009.…”

Section: Introductionmentioning

confidence: 99%

Double Kernel Method Using Line Transect Sampling

Shakhatreh

2016

AJS

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.