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December 2019 A latent discrete Markov random field approach to identifying and classifying historical forest communities based on spatial multivariate tree species counts
Stephen Berg, Jun Zhu, Murray K. Clayton, Monika E. Shea, David J. Mladenoff
Ann. Appl. Stat. 13(4): 2312-2340 (December 2019). DOI: 10.1214/19-AOAS1259

Abstract

The Wisconsin Public Land Survey database describes historical forest composition at high spatial resolution and is of interest in ecological studies of forest composition in Wisconsin just prior to significant Euro-American settlement. For such studies it is useful to identify recurring subpopulations of tree species known as communities, but standard clustering approaches for subpopulation identification do not account for dependence between spatially nearby observations. Here, we develop and fit a latent discrete Markov random field model for the purpose of identifying and classifying historical forest communities based on spatially referenced multivariate tree species counts across Wisconsin. We show empirically for the actual dataset and through simulation that our latent Markov random field modeling approach improves prediction and parameter estimation performance. For model fitting we introduce a new stochastic approximation algorithm which enables computationally efficient estimation and classification of large amounts of spatial multivariate count data.

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Stephen Berg. Jun Zhu. Murray K. Clayton. Monika E. Shea. David J. Mladenoff. "A latent discrete Markov random field approach to identifying and classifying historical forest communities based on spatial multivariate tree species counts." Ann. Appl. Stat. 13 (4) 2312 - 2340, December 2019. https://doi.org/10.1214/19-AOAS1259

Information

Received: 1 August 2018; Revised: 1 March 2019; Published: December 2019
First available in Project Euclid: 28 November 2019

zbMATH: 07160941
MathSciNet: MR4037432
Digital Object Identifier: 10.1214/19-AOAS1259

Keywords: historical ecology , Markov random field , maximum likelihood , Mixture models , stochastic approximation

Rights: Copyright © 2019 Institute of Mathematical Statistics

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Vol.13 • No. 4 • December 2019
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