A pliant parametric detection model for line transect data sampling

Abstract

Line transect survey methodology is a commonly used method for estimating the population abundance. Despite recent advances in this regard, parametric models are still widely used among biometricians, mainly because of their simplicity. In this paper, a new two-parameter detection model satisfying the shoulder conditions is proposed for modeling line transect data. We discuss its properties of interest, including the shapes of the model and the corresponding probability density function, moments, and the related sub-detection model. Maximum likelihood estimation of the parameters is considered. Subsequently, an application is carried out to the proposed model based on a practical data set of perpendicular distances. It is compared with some classical and recent models based on the evaluation of some goodness-of-fit statistics. As results, the variance-covariance matrix, confidence intervals of the parameters and estimated population abundance of the data set are obtained under the proposed detection model.