A gamma-poisson distribution of point to k nearest event distance

Magnussen Steen, Picard Nicolas, Kleinn Christoph. 2008. A gamma-poisson distribution of point to k nearest event distance. Forest Science, 54 (4) : 429-441.

Résumé : Distance sampling of events in natural or seminatural populations often indicates a larger variance in the distance to the kth nearest event than expected for events distributed completely at random. Overdispersion contributes to the well-known bias problem of distance sampling density estimators. Distance distribution models that accommodate overdispersion in the data should lead to more robust estimators of density. To this end we propose a gamma-Poisson distribution model for distances from a point to k nearest events. The model assumes a gamma distribution of local densities of randomly distributed events. Properties of the distribution and estimation of the parameters and event density are detailed for both constrained and unconstrained sampling. Four examples, one with simulated data from a known negative binomial distribution and three with simulated distance sampling in natural and seminatural stem-mapped tree stands, illustrate the promising performance of this new distribution, both as a model for distances and for density estimation. The modeling approach extends to other mixing distributions.

Mots-clés Agrovoc : modèle de simulation, modèle mathématique, méthode statistique, espacement, peuplement forestier, échantillonnage, dynamique des populations, forêt, population végétale

Mots-clés complémentaires : Forêt naturelle

Classification Agris : U10 - Informatique, mathématiques et statistiques
K10 - Production forestière

Champ stratégique Cirad : Hors axes (2005-2013)

Auteurs et affiliations

• Magnussen Steen, Natural Resources Canada (CAN)
• Picard Nicolas, CIRAD-ES-UPR Dynamique forestière (GAB)
• Kleinn Christoph, Georg-August University of Göttingen (DEU)

Source : Cirad - Agritrop (https://agritrop.cirad.fr/547562/)

Métriques

[ Page générée et mise en cache le 2024-12-18 ]