Markov and Semi-Markov Switching Linear Mixed Models Used to Identify Forest Tree Growth Components
Article first published online: 13 NOV 2009
© 2009, The International Biometric Society
Volume 66, Issue 3, pages 753–762, September 2010
How to Cite
Chaubert-Pereira, F., Guédon, Y., Lavergne, C. and Trottier, C. (2010), Markov and Semi-Markov Switching Linear Mixed Models Used to Identify Forest Tree Growth Components. Biometrics, 66: 753–762. doi: 10.1111/j.1541-0420.2009.01338.x
- Issue published online: 13 NOV 2009
- Article first published online: 13 NOV 2009
- Received October 2008. Revised August 2009. Accepted August 2009.
- Individual random effect;
- Markov switching model;
- MCEM algorithm;
- Plant structure analysis;
- Semi-Markov switching model
Summary Tree growth is assumed to be mainly the result of three components: (i) an endogenous component assumed to be structured as a succession of roughly stationary phases separated by marked change points that are asynchronous among individuals, (ii) a time-varying environmental component assumed to take the form of synchronous fluctuations among individuals, and (iii) an individual component corresponding mainly to the local environment of each tree. To identify and characterize these three components, we propose to use semi-Markov switching linear mixed models, i.e., models that combine linear mixed models in a semi-Markovian manner. The underlying semi-Markov chain represents the succession of growth phases and their lengths (endogenous component) whereas the linear mixed models attached to each state of the underlying semi-Markov chain represent—in the corresponding growth phase—both the influence of time-varying climatic covariates (environmental component) as fixed effects, and interindividual heterogeneity (individual component) as random effects. In this article, we address the estimation of Markov and semi-Markov switching linear mixed models in a general framework. We propose a Monte Carlo expectation–maximization like algorithm whose iterations decompose into three steps: (i) sampling of state sequences given random effects, (ii) prediction of random effects given state sequences, and (iii) maximization. The proposed statistical modeling approach is illustrated by the analysis of successive annual shoots along Corsican pine trunks influenced by climatic covariates.