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Markov and semi-Markov switching linear mixed models for identifying forest tree growth components.

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Academic year: 2021

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Figure

Figure 1: Four sub-samples of Corsican pines: Length of successive annual shoots along tree trunks.
Figure 2: Values of the difference of observed-data log-likelihood given ran- ran-dom effects between successive iterations (Equation 11) for the estimated  semi-Markov switching linear mixed model with individual-state-wise random effects.
Figure 3: Estimated underlying semi-Markov chain. Each state is represented by a vertex which is numbered
Table 1: Comparison of the estimated Gaussian hidden semi-Markov chain (GHSMC) parameters with estimated semi-Markov switching linear mixed model (SMS-LMM) parameters (state occupancy distributions and marginal observation distributions)
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