Local likelihood density estimation in line transect sampling

A novel semiparametric estimator for the probability density function of detected distances in line transect sampling is proposed. The estimator is obtained using a local likelihood density estimation approach, a technique recently proposed which affords the advantages of both parametric and nonparametric methods, i.e. accuracy and robustness. Moreover, a procedure for the selection of the local likelihood bandwidth is obtained. The performance of the proposed estimator with respect to some existing nonparametric and semiparametric estimators is assessed by means of a Monte Carlo study. Finally, a real data set is analyzed. Copyright (C) 2000 John Wiley & Sons, Ltd.