The uniformly most powerful invariant test for two models of detection function in point transect sampling
| Autor: | Riccardo Borgoni, Piero Quatto |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2013 |
| Předmět: | |
| Zdroj: | Statistica, Vol 69, Iss 1, Pp 3-14 (2013) |
| Druh dokumentu: | article |
| ISSN: | 0390-590X 1973-2201 |
| DOI: | 10.6092/issn.1973-2201/3544 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
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