The utility of less-common statistical methods for analyzing agricultural systems: focus on kernel density estimation, copula modeling and extreme value theory

Tristan Senga Kiesse; Michael Corson

doi:10.1007/s41237-022-00190-y

Journal Articles Behaviormetrika Year : 2022

The utility of less-common statistical methods for analyzing agricultural systems: focus on kernel density estimation, copula modeling and extreme value theory

(1) ,

1

Tristan Senga Kiesse

Function : Author
PersonId : 1888
IdHAL : tristan-senga-kiesse
ORCID : 0000-0003-2710-5825
IdRef : 130263206

Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement

CV

Michael Corson

Function : Author

Keywords

Extreme value theory Greenhouse gas emissions Kernel density estimation Cumulative distribution function Probability density function Livestock unit

Domains

Mathematics [math] Environmental Sciences

Tristan Senga Kiessé : Connect in order to contact the contributor

https://hal.inrae.fr/hal-03940388

Submitted on : Monday, January 16, 2023-10:18:06 AM

Last modification on : Tuesday, January 17, 2023-4:14:40 AM

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hal-03940388 , version 1 (16-01-2023)

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HAL Id : hal-03940388 , version 1
DOI : 10.1007/s41237-022-00190-y

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Tristan Senga Kiesse, Michael Corson. The utility of less-common statistical methods for analyzing agricultural systems: focus on kernel density estimation, copula modeling and extreme value theory. Behaviormetrika, 2022, ⟨10.1007/s41237-022-00190-y⟩. ⟨hal-03940388⟩

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The utility of less-common statistical methods for analyzing agricultural systems: focus on kernel density estimation, copula modeling and extreme value theory

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