Open AccessAn Adaptation of Kernel Density Estimation for Population Abundance using Line Transect Sampling When the Shoulder Condition is Violated
Author(s) -
Baker Ishaq Albadareen,
Noriszura Ismail
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.b6582.129219
Subject(s) - estimator , kernel density estimation , mathematics , kernel (algebra) , variable kernel density estimation , statistics , transect , multivariate kernel density estimation , population , kernel method , computer science , combinatorics , ecology , artificial intelligence , demography , sociology , support vector machine , biology
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