Bias correction for histogram estimator using line transect sampling

This article proposes a simple approach for reducing the bias of the traditional histogram estimator using line transect sampling. The approach uses the bias correction technique, which produces a new estimator for density of objects D. The proposed estimator reduces the bias from O(h(2)) to O(h(3)) as h -> 0 under the shoulder condition assumption. The asymptotic properties of the proposed estimator are derived under some mild assumptions, and the optimal formula for the bin width is given. Small-sample properties of the proposed estimator are studied and compared with some other existence estimators by using a simulation technique. The results show that improvements over the traditional histogram estimator often can be realized even at small or moderate sample size. Copyright (c) 2004 John Wiley & Sons, Ltd.