Skip to Main content Skip to Navigation
Journal articles

Splitting models for multivariate count data

Abstract : We investigate the class of splitting distributions as the composition of a singular multivariate distribution and a univariate distribution. It will be shown that most common parametric count distributions (multinomial, negative multinomial, multivariate hypergeometric, multivariate negative hypergeometric, …) can be written as splitting distributions with separate parameters for both components, thus facilitating their interpretation, inference, the study of their probabilistic characteristics and their extensions to regression models. We highlight many probabilistic properties deriving from the compound aspect of splitting distributions and their underlying algebraic properties. Parameter inference and model selection are thus reduced to two separate problems, preserving time and space complexity of the base models. Based on this principle, we introduce several new distributions. In the case of multinomial splitting distributions, conditional independence and asymptotic normality properties for estimators are obtained. Mixtures of splitting regression models are used on a mango tree dataset in order to analyze the patchiness.
Document type :
Journal articles
Complete list of metadatas

Cited literature [55 references]  Display  Hide  Download

https://hal.inria.fr/hal-02962877
Contributor : Jean-Baptiste Durand <>
Submitted on : Friday, October 9, 2020 - 3:30:11 PM
Last modification on : Friday, October 16, 2020 - 3:08:56 AM

File

main.pdf
Files produced by the author(s)

Identifiers

Citation

Jean Peyhardi, Pierre Fernique, Jean-Baptiste Durand. Splitting models for multivariate count data. Journal of Multivariate Analysis, Elsevier, 2021, 181, pp.104677. ⟨10.1016/j.jmva.2020.104677⟩. ⟨hal-02962877⟩

Share

Metrics

Record views

37

Files downloads

53