title: Application of change-point problem to the detection of plant patches.
abstract: In ecology, if the considered area or space is large, the spatial distribution of individuals of a given plant species is never homogeneous; plants form different patches. The homogeneity change in space or in time (in particular, the related change-point problem) is an important research subject in mathematical statistics. In the paper, for a given data system along a straight line, two areas are considered, where the data of each area come from different discrete distributions, with unknown parameters. In the paper a method is presented for the estimation of the distribution change-point between both areas and an estimate is given for the distributions separated by the obtained change-point. The solution of this problem will be based on the maximum likelihood method. Furthermore, based on an adaptation of the well-known bootstrap resampling, a method for the estimation of the so-called change-interval is also given. The latter approach is very general, since it not only applies in the case of the maximum-likelihood estimation of the change-point, but it can be also used starting from any other change-point estimation known in the ecological literature. The proposed model is validated against typical ecological situations, providing at the same time a verification of the applied algorithms.
authors.affiliation: Department of Statistics and Applied Mathematics, University of Almería, La Cañada de San Urbano, s/n, 04120 Almería, Spain. milopez@ual.es
authors.forename: I
authors.initials: I
authors.lastname: López
authors.forename: M
authors.initials: M
authors.lastname: Gámez
authors.forename: J
authors.initials: J
authors.lastname: Garay
authors.forename: T
authors.initials: T
authors.lastname: Standovár
authors.forename: Z
authors.initials: Z
authors.lastname: Varga
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003196:Computer Graphics; D003198:Computer Simulation; D004463:Ecology; D007090:Image Interpretation, Computer-Assisted; D016013:Likelihood Functions; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated; D018521:Plant Physiological Phenomena; D010944:Plants / Q000378:metabolism*; D049490:Systems Biology