| Autor: |
Stephan R. Sain, Michelle M. Steen-Adams, Jun Zhu, Chongyang Jin, Ronald E. Gangnon |
| Rok vydání: |
2013 |
| Předmět: |
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| Zdroj: |
Environmetrics. 24:98-108 |
| ISSN: |
1180-4009 |
| DOI: |
10.1002/env.2189 |
| Popis: |
We develop statistical tools for regression analysis of nominal categorical data on a spatial lattice that are becoming increasingly abundant because of the advances of geographic information systems in environmental science. In a generalized linear mixed model framework, we model the response variable by a multinomial distribution. There are two additive components in the linear predictor: a linear regression on covariates and a spatial random effect such that the spatial dependence in the random effect is induced by a multivariate conditional autoregressive model. Bayesian hierarchical modeling is used for statistical inference, and Markov chain Monte Carlo algorithms are devised to obtain posterior samples. The methodology is applied to analyze a northern Wisconsin land cover data set in a study that assesses the relationship between forest landscape structure and past social conditions, expanding the analytical tools available in landscape ecology and environmental history. Copyright © 2013 John Wiley & Sons, Ltd. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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