Abstract
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.
Highlights
Line transect sampling is considered as a common technique to estimate population abundance
The intuitive condition in the line transect sampling is that the probability of detecting an object, g(x), is a conditional and non-increasing function of exposing an object given that the object is far away from the line by distance x
We propose the power transformation with general base form and apply the transformation on the kernel estimator when fX′(0) ≠ 0
Summary
Line transect sampling is considered as a common technique to estimate population abundance (density). For this method, the study area A divides into non-overlapping parts (strips) with total length L, assuming that an observer follows each strip and records the perpendicular distance of each detected animal (object). Assume that a random sample of the line transect method with non-negative distances are x1, x2, ... A general base of power-transformation of perpendicular distance under the kernel method is proposed for the population density when the shoulder condition is violated. The efficiency results are supported by simulation studies, and the performance comparison is carried out between the proposed estimator and the classical kernel estimator
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