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Modeling a Poisson Forest in Variable Elevations: A Nonparametric Bayesian Approach
Author(s) -
Heikkinen Juha,
Arjas Elja
Publication year - 1999
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.0006-341x.1999.00738.x
Subject(s) - nonparametric statistics , markov chain monte carlo , statistics , poisson distribution , markov chain , mathematics , bayesian probability , econometrics , monte carlo method , computer science
Summary. 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.