Combination of parametric and nonparametric estimators for population abundance using line transect sampling. Issue 7 (3rd October 2018)

• Record Type: Journal Article
• Authors: Al-Bassam, Mohammad; Eidous, Omar
• Is Part Of: Journal of information & optimization sciences. Volume 39:Issue 7(2018)
• Page Start: 1449
• Page End: 1462
• Publication Date: 2018-10-03
• Subjects: Line Transect Sampling -- Akaike Information Criterion -- Kernel Method -- Semi-Parametric Method -- Smoothing Parameter; Electronic data processing -- Periodicals; Information science -- Periodicals; Mathematical optimization -- Periodicals; 519.6
• DOI: 10.1080/02522667.2017.1367510
• Languages: English
• ISSNs: 0252-2667

Abstract

Abstract: This paper introduced a modification of the classical kernel estimator for population abundance using line transect sampling. The procedure allows the incorporation of a small set of parametric models with the usual kernel estimator. Akaike Information Criterion (AIC) is used to choose the most appropriate parametric detection function. The resultant estimators of f (0); the probability density function at perpendicular distance x = 0, are compared with the existing usual kernel estimator by using the simulation technique and a set of real data. Numerical results demonstrate the superiority of these estimators over the usual kernel estimator for almost all considered cases.