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A New Zero-Inflated Negative Binomial Multilevel Model for Forecasting the Demand of Disaster Relief Supplies in the State of Sao Paulo, Brazil

This article presents the results of the implementation of a forecasting model, to predict the relief materials needed for assisting in decisions prior to natural disasters, thus filling a gap in the exploration of Generalized Linear Mixed Models (GLMM) in a humanitarian context. Demand information...

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Hlavní autoři: Camila Pareja Yale, Hugo Tsugunobu Yoshida Yoshizaki, Luiz Paulo Fávero
Médium: Artigo
Jazyk:Inglês
Vydáno: MDPI AG 2022-11-01
Edice:Mathematics
Témata:
zero-inflated models
count data models
multilevel models
hierarchical models
random effects
nested models
On-line přístup:https://www.mdpi.com/2227-7390/10/22/4352
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