A new approach to modeling positive random variables with repeated measures

Autor(es)

de Freitas, Joao Victor B. ^[1] ; Nobre, Juvencio S. ^[2] ; Bourguignon, Marcelo ^[3] ; Santos-Neto, Manoel ^[4]

Afiliação do(s) autor(es)

^[1] Univ Estadual Campinas, Inst Matemat Estat & Comp Cient, Dept Estat, Campinas, SP - Brazil

^[2] Univ Fed Ceara, Dept Estat & Matemat Aplicada, Fortaleza, Ceara - Brazil

^[3] Univ Fed Rio Grande do Norte, Dept Estat, Natal, RN - Brazil

^[4] Univ Fed Campina Grande, Dept Estat, Campina Grande, Paraiba - Brazil

• Tipo de documento: Artigo Científico
• Fonte: Journal of Applied Statistics; AUG 2021.
• Citações Web of Science: 0

Resumo

In many situations, it is common to have more than one observation per experimental unit, thus generating the experiments with repeated measures. In the modeling of such experiments, it is necessary to consider and model the intra-unit dependency structure. In the literature, there are several proposals to model positive continuous data with repeated measures. In this paper, we propose one more with the generalization of the beta prime regression model. We consider the possibility of dependence between observations of the same unit. Residuals and diagnostic tools also are discussed. To evaluate the finite-sample performance of the estimators, using different correlation matrices and distributions, we conducted a Monte Carlo simulation study. The methodology proposed is illustrated with an analysis of a real data set. Finally, we create an R package for easy access to publicly available the methodology described in this paper. (AU)

• Processo FAPESP: 13/07375-0 - CeMEAI - Centro de Ciências Matemáticas Aplicadas à Indústria
• Beneficiário: Francisco Louzada Neto
• Modalidade de apoio: Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs