Publication

Statistical inference for olfactometer data

Anthony Christopher Davison, Ingrid Ricard
2007
Journal Articles

Résumé

Olfactometer experiments are used to determine the effect of odours on the behaviour of organisms such as insects or nematodes, and typically result in data comprising many groups of small overdispersed counts. We develop a non-homogeneous Markov chain model for data from olfactometer experiments with parasitoid wasps, and discuss a relation with the Dirichlet-multinomial distribution. We consider the asymptotic relative efficiencies of three different sampling schemes, and give an analysis of data intended to shed light on the effect of previous experience of odours in parasitoid wasps.

Source officielle

https://infoscience.epfl.ch/entities/publication/c96913df-ae3f-4055-b9cf-18c7a1d2c4c6

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Anthony Christopher Davison, Ingrid Ricard
2007
Journal Articles

Résumé

Source officielle

https://infoscience.epfl.ch/entities/publication/c96913df-ae3f-4055-b9cf-18c7a1d2c4c6

À propos de ce résultat

Concepts associés (25)

Dirichlet-multinomial distribution

In probability theory and statistics, the Dirichlet-multinomial distribution is a family of discrete multivariate probability distributions on a finite support of non-negative integers. It is also called the Dirichlet compound multinomial distribution (DCM) or multivariate Pólya distribution (after George Pólya). It is a compound probability distribution, where a probability vector p is drawn from a Dirichlet distribution with parameter vector , and an observation drawn from a multinomial distribution with probability vector p and number of trials n.

Loi de Dirichlet

thumb|right|250px|Plusieurs images de la densité de la loi de Dirichlet lorsque K=3 pour différents vecteurs de paramètres α. Dans le sens horaire à partir du coin supérieur gauche : α=(6, 2, 2), (3, 7, 5), (6, 2, 6), (2, 3, 4). En probabilité et statistiques, la loi de Dirichlet, souvent notée Dir(α), est une famille de lois de probabilité continues pour des variables aléatoires multinomiales. Cette loi (ou encore distribution) est paramétrée par le vecteur α de nombres réels positifs et tire son nom de Johann Peter Gustav Lejeune Dirichlet.

Dirichlet negative multinomial distribution

In probability theory and statistics, the Dirichlet negative multinomial distribution is a multivariate distribution on the non-negative integers. It is a multivariate extension of the beta negative binomial distribution. It is also a generalization of the negative multinomial distribution (NM(k, p)) allowing for heterogeneity or overdispersion to the probability vector. It is used in quantitative marketing research to flexibly model the number of household transactions across multiple brands.

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