title: Modeling a Poisson forest in variable elevations: a nonparametric Bayesian approach.
abstract: A nonparametric Bayesian formulation is given to the problem of modeling nonhomogeneous spatial point patterns influenced by concomitant variables. Only incomplete information on the concomitant variables is assumed, consisting of a relatively small number of point measurements. Residual variation, caused by other unmeasured influential factors, is modeled in terms of a spatially varying baseline intensity function. A Markov chain Monte Carlo scheme is proposed for the simultaneous nonparametric estimation of each unknown function in the model. The suggested method is illustrated by reanalysing a data set in Rathbun (1996, Biometrics 52, 226-242), and the estimated models are compared with those obtained by Rathbun.
authors: Finnish Forest Research Institute, Helsinki. Juha.Heikkinen@metla.fi
authors: J
authors: J
authors: Heikkinen
authors: E
authors: E
authors: Arjas
mesh_terms: D000531:Altitude; D001499:Bayes Theorem; D001699:Biometry; D017753:Ecosystem; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method; D016012:Poisson Distribution; D014197:Trees