Bayesian prediction of spatial count data using generalized linear mixed models.

Abstract:

:Spatial weed count data are modeled and predicted using a generalized linear mixed model combined with a Bayesian approach and Markov chain Monte Carlo. Informative priors for a data set with sparse sampling are elicited using a previously collected data set with extensive sampling. Furthermore, we demonstrate that so-called Langevin-Hastings updates are useful for efficient simulation of the posterior distributions, and we discuss computational issues concerning prediction.

journal_name

Biometrics

journal_title

Biometrics

authors

Christensen OF,Waagepetersen R

doi

10.1111/j.0006-341x.2002.00280.x

subject

Has Abstract

pub_date

2002-06-01 00:00:00

pages

280-6

issue

2

eissn

0006-341X

issn

1541-0420

journal_volume

58

pub_type

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