On Modeling Wood Formation Using Parametric And Semiparametric Regressions For Count Data - Ecole Centrale de Nantes Accéder directement au contenu
Article Dans Une Revue Communications in Statistics - Simulation and Computation Année : 2014

On Modeling Wood Formation Using Parametric And Semiparametric Regressions For Count Data

Résumé

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.
Fichier principal
Vignette du fichier
CunySengaK2015.pdf (507.34 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-01097990 , version 1 (25-10-2021)

Licence

Paternité

Identifiants

Citer

Henri H. Cuny, Tristan Senga Kiessé. On Modeling Wood Formation Using Parametric And Semiparametric Regressions For Count Data. Communications in Statistics - Simulation and Computation, 2014, 45 (5), pp.1748-1762. ⟨10.1080/03610918.2013.875570⟩. ⟨hal-01097990⟩
154 Consultations
47 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More