Density estimation for grouped data with application to line transect sampling

TL;DR

This paper introduces a kernel density estimator tailored for grouped data from line transect sampling, improving density estimation accuracy and confidence interval construction in wildlife population studies.

Contribution

It proposes a novel combined cross-validation and smoothed bootstrap method for optimal bandwidth selection in grouped data density estimation.

Findings

Estimated densities closely match true densities in simulations

Method provides bias-adjusted confidence intervals at boundaries

Application to real data demonstrates practical utility

Abstract

Line transect sampling is a method used to estimate wildlife populations, with the resulting data often grouped in intervals. Estimating the density from grouped data can be challenging. In this paper we propose a kernel density estimator of wildlife population density for such grouped data. Our method uses a combined cross-validation and smoothed bootstrap approach to select the optimal bandwidth for grouped data. Our simulation study shows that with the smoothing parameter selected with this method, the estimated density from grouped data matches the true density more closely than with other approaches. Using smoothed bootstrap, we also construct bias-adjusted confidence intervals for the value of the density at the boundary. We apply the proposed method to two grouped data sets, one from a wooden stake study where the true density is known, and the other from a survey of kangaroos in Australia.