@article{Borgoni_Quatto_2009, title={The uniformly most powerful invariant test for two models of detection function in point transect sampling}, volume={69}, url={https://rivista-statistica.unibo.it/article/view/3544}, DOI={10.6092/issn.1973-2201/3544}, abstractNote={Estimating population abundance is of primary interest in wildlife population studies. Point transect sampling is a well established methodology for this purpose. The usual approach for estimating the density or the size of the population of interest is to assume a particular model for the detection function (the conditional probability of detecting an animal given that it is at a certain distance from the observer). Two popular models for this function are the half-normal model and the negative exponential model. However, it appears that the estimates are extremely sensitive to the shape of the detection function, particularly to the so-called shoulder condition, which ensures that an animal is nearly certain to be detected if it is at a small distance from the observer. The half-normal model satisfies this condition whereas the negative exponential does not. Testing whether such a hypothesis is consistent with the data at hand should be a primary concern. Given that the problem of testing the shoulder condition of a detection function is invariant under the group of scale transformations, in this paper we propose the uniformly most powerful.}, number={1}, journal={Statistica}, author={Borgoni, Riccardo and Quatto, Piero}, year={2009}, month={Jan.}, pages={3–14} }