  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.
