Modeling a Poisson Forest in Variable Elevations: A Nonparametric Bayesian Approach
- 1 September 1999
- journal article
- Published by Oxford University Press (OUP) in Biometrics
- Vol. 55 (3) , 738-745
Abstract
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, Biometrics52, 226–242), and the estimated models are compared with those obtained by Rathbun.Keywords
This publication has 6 references indexed in Scilit:
- Non‐parametric Bayesian Estimation of a Spatial Poisson IntensityScandinavian Journal of Statistics, 1998
- Curve and Surface Estimation Using Dynamic Step Functions1998
- Estimation of Poisson Intensity Using Partially Observed Concomitant Variables1996
- Reversible jump Markov chain Monte Carlo computation and Bayesian model determinationBiometrika, 1995
- Bayesian Computation and Stochastic SystemsStatistical Science, 1995
- Practical Markov Chain Monte CarloStatistical Science, 1992