The odd log-logistic geometric normal regression model with applications (2019)
- Authors:
- USP affiliated authors: ORTEGA, EDWIN MOISES MARCOS - ESALQ ; SUZUKI, ADRIANO KAMIMURA - ICMC
- Unidades: ESALQ; ICMC
- DOI: 10.1142/S2424922X19500037
- Subjects: REGRESSÃO LOGÍSTICA; DISTRIBUIÇÃO NORMAL; REGRESSÃO LINEAR
- Keywords: GAMLSS; odd log-logistic geometric family; regression model; residual analysis
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título: Advances in Data Science and Adaptive Analysis
- ISSN: 2424-922X
- Volume/Número/Paginação/Ano: v. 11, n. 1, p. 1950003-1-1950003-25, Apr. 2019
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
PRATAVIERA, Fábio et al. The odd log-logistic geometric normal regression model with applications. Advances in Data Science and Adaptive Analysis, v. 11, n. 1, p. 1950003-1-1950003-25, 2019Tradução . . Disponível em: https://doi.org/10.1142/S2424922X19500037. Acesso em: 04 out. 2024. -
APA
Prataviera, F., Cordeiro, G. M., Ortega, E. M. M., Suzuki, A. K., & Amaro Jr., E. (2019). The odd log-logistic geometric normal regression model with applications. Advances in Data Science and Adaptive Analysis, 11( 1), 1950003-1-1950003-25. doi:10.1142/S2424922X19500037 -
NLM
Prataviera F, Cordeiro GM, Ortega EMM, Suzuki AK, Amaro Jr. E. The odd log-logistic geometric normal regression model with applications [Internet]. Advances in Data Science and Adaptive Analysis. 2019 ; 11( 1): 1950003-1-1950003-25.[citado 2024 out. 04 ] Available from: https://doi.org/10.1142/S2424922X19500037 -
Vancouver
Prataviera F, Cordeiro GM, Ortega EMM, Suzuki AK, Amaro Jr. E. The odd log-logistic geometric normal regression model with applications [Internet]. Advances in Data Science and Adaptive Analysis. 2019 ; 11( 1): 1950003-1-1950003-25.[citado 2024 out. 04 ] Available from: https://doi.org/10.1142/S2424922X19500037 - Bivariate odd-log-logistic-Weibull regression model for oral health-related quality of life
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Informações sobre o DOI: 10.1142/S2424922X19500037 (Fonte: oaDOI API)
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