On Modeling Wood Formation Using Parametric and Semiparametric Regressions for Count Data |
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Authors: | Henri E. Cuny |
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Affiliation: | INRA, UMR1092, Laboratoire d Etude des Ressources Foret Bois (LERFoB), Centre INRA de Nancy, Champenoux, France |
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Abstract: | Understanding how wood develops has become an important problematic of plant sciences. However, studying wood formation requires the acquisition of count data difficult to interpret. Here, the annual wood formation dynamics of a conifer tree species were modeled using generalized linear and additive models (GLM and GAM); GAM for location, scale, and shape (GAMLSS); a discrete semiparametric kernel regression for count data. The performance of models is evaluated using bootstrap methods. GLM was useful to describe the wood formation general pattern but had a lack of fitting, while GAM, GAMLSS, and kernel regression had a higher sensibility to short-term variations. |
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Keywords: | Bootstrap resampling methods Count regression function Discrete kernel Generalized linear and additive models Optimal bandwidth selections Wood formation |
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