Modeling a Poisson forest in variable elevations: a nonparametric Bayesian approach.

J Heikkinen; E Arjas

doi:10.1111/j.0006-341x.1999.00738.x

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Modeling a Poisson forest in variable elevations: a nonparametric Bayesian approach.

J Heikkinen·E Arjas

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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.

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J Heikkinen, E Arjas. 1999. Modeling a Poisson forest in variable elevations: a nonparametric Bayesian approach.. https://doi.org/10.1111/j.0006-341x.1999.00738.x

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