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Modeling in forestry using mixture models fitted to grouped and ungrouped data / Eric K. Zenner in Forests, vol 12 n° 9 (September 2021)  

Public [article]  inForests > vol 12 n° 9 (September 2021) . - n° 1196 Titre : Modeling in forestry using mixture models fitted to grouped and ungrouped data Type de document : Article/Communication Auteurs : Eric K. Zenner, Auteur ; Mahdi Teimouri, Auteur Année de publication : 2021 Article en page(s) : n° 1196 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques [Termes IGN] algorithme espérance-maximisation [Termes IGN] complexité [Termes IGN] diamètre à hauteur de poitrine [Termes IGN] distribution de Weibull [Termes IGN] distribution, loi de [Termes IGN] dynamique de la végétation [Termes IGN] estimation par noyau [Termes IGN] méthode du maximum de vraisemblance (estimation) [Termes IGN] modélisation de la forêt [Termes IGN] structure d'un peuplement forestier Résumé : (auteur) The creation and maintenance of complex forest structures has become an important forestry objective. Complex forest structures, often expressed in multimodal shapes of tree size/diameter (DBH) distributions, are challenging to model. Mixture probability density functions of two- or three-component gamma, log-normal, and Weibull mixture models offer a solution and can additionally provide insights into forest dynamics. Model parameters can be efficiently estimated with the maximum likelihood (ML) approach using iterative methods such as the Newton-Raphson (NR) algorithm. However, the NR algorithm is sensitive to the choice of initial values and does not always converge. As an alternative, we explored the use of the iterative expectation-maximization (EM) algorithm for estimating parameters of the aforementioned mixture models because it always converges to ML estimators. Since forestry data frequently occur both in grouped (classified) and ungrouped (raw) forms, the EM algorithm was applied to explore the goodness-of-fit of the gamma, log-normal, and Weibull mixture distributions in three sample plots that exhibited irregular, multimodal, highly skewed, and heavy-tailed DBH distributions where some size classes were empty. The EM-based goodness-of-fit was further compared against a nonparametric kernel-based density estimation (NK) model and the recently popularized gamma-shaped mixture (GSM) models using the ungrouped data. In this example application, the EM algorithm provided well-fitting two- or three-component mixture models for all three model families. The number of components of the best-fitting models differed among the three sample plots (but not among model families) and the mixture models of the log-normal and gamma families provided a better fit than the Weibull distribution for grouped and ungrouped data. For ungrouped data, both log-normal and gamma mixture distributions outperformed the GSM model and, with the exception of the multimodal diameter distribution, also the NK model. The EM algorithm appears to be a promising tool for modeling complex forest structures. Numéro de notice : A2021-721 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.3390/f12091196 En ligne : https://doi.org/10.3390/f12091196 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98639 [article]