scholarly journals A General Base of Power Transformation to Improve the Boundary Effect in Kernel Density without Shoulder Condition

2020 ◽  
pp. 279-285
Author(s):  
Baker Ishaq Albadareen ◽  
Noriszura Ismail
Keyword(s):  
Mean Squared Error ◽  
Kernel Method ◽  
Boundary Effect ◽  
Kernel Estimator ◽  
Line Transect ◽  
Power Transformation ◽  
General Base ◽  
Transect Sampling ◽  
Absolute Bias ◽  
Shoulder Condition

In this paper, a general base of power transformation under the kernel method is suggested and applied in the line transect sampling to estimate abundance. The suggested estimator performs well at the boundary compared to the classical kernel estimator without using the shoulder condition assumption. The transformed estimator show smaller value of mean squared error and absolute bias from the efficiency results obtained using simulation.

scholarly journals Double Kernel Method Using Line Transect Sampling

2016 ◽  
Vol 41 (2) ◽  
Author(s):  
Omar Eidous ◽  
M.K. Shakhatreh
Keyword(s):  
Kernel Method ◽  
Kernel Estimator ◽  
Real Data ◽  
Line Transect ◽  
Population Abundance ◽  
Line Transect Sampling ◽  
Numerical Examples ◽  
Bootstrap Techniques ◽  
Transect Sampling ◽  
Kernel Type

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 existingclassical kernel estimator in most considered cases.

scholarly journals An Adaptation of Kernel Density Estimation for Population Abundance using Line Transect Sampling When the Shoulder Condition is Violated

2019 ◽  
Vol 9 (2) ◽  
pp. 3494-3498
Keyword(s):  
Kernel Estimation ◽  
Kernel Density ◽  
Kernel Estimator ◽  
Line Transect ◽  
Population Abundance ◽  
Line Transect Sampling ◽  
Mathematical Properties ◽  
Perpendicular Distance ◽  
Transect Sampling ◽  
Shoulder Condition

Kernel estimation is a commonly used method to estimate the population density in line transect sampling. In general, the classical kernel estimator of (0) X f , which is the probability density function at perpendicular distance x  0 , inclines to be underestimated. In this study, a power transformation of perpendicular distance is proposed for the kernel estimator when the shoulder condition is violated. The mathematical properties of the proposed estimator are derived. A simulation study is also carried out for comparing the proposed estimator with the classical kernel estimators

scholarly journals Performance of Parametric Model for Line Transect Data

2020 ◽  
pp. 1-7
Author(s):  
Noryanti Muhammad ◽  
Gamil A.A. Saeed ◽  
Wan Nur Syahidah Wan Yusoff
Keyword(s):  
Simulation Study ◽  
Present Model ◽  
Parametric Model ◽  
Line Transect ◽  
Population Abundance ◽  
Line Transect Sampling ◽  
Detection Function ◽  
Transect Sampling ◽  
Proposed Model ◽  
Shoulder Condition

One of the most important sides of life is wildlife. There is growing research interest in monitoring wildlife. Line transect sampling is one of the techniques widely used for estimating the density of objects especially for animals and plants. In this research, a parametric estimator for estimation of the population abundance is developed. A new parametric model for perpendicular distances for detection function is utilised to develop the estimator. In this paper, the performance of the parametric model which was developed using a simulation study is presented. The detection function has non-increasing curve and a perfect probability at zero. Theoretically, the parametric model which has been developed is guar-anteed to satisfy the shoulder condition assumption. A simulation study is presented to validate the present model. Relative mean error (RME) and Relative Bias (RB) are used to compare the estimator with well-known existing estimators. The results of the simulation study are discussed, and the performance of the proposed model shows promising statistical properties which outperformed the existing models. Keywords: detection function, line transect data, parametric model

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