Bivariate odd-log-logistic-Weibull regression model for oral health-related quality of life

Bivariate odd-log-logistic-Weibull regression model for oral health-related quality of life (2017)

Authors:
USP affiliated authors: ORTEGA, EDWIN MOISES MARCOS - ESALQ ; SUZUKI, ADRIANO KAMIMURA - ICMC
Unidades: ESALQ; ICMC
DOI: 10.5351/CSAM.2017.24.3.271
Subjects: PROBABILIDADE; INFERÊNCIA BAYESIANA; INFERÊNCIA PARAMÉTRICA; INFERÊNCIA ESTATÍSTICA
Keywords: Clayton copula; Frank copula; local influence; regression model; Weibull distribution
Language: Inglês
Imprenta:
- Publisher place: Seoul, Korea
- Date published: 2017
Source:
- Título: Communications for Statistical Applications and Methods
- ISSN: 2287-7843
- Volume/Número/Paginação/Ano: v. 24, n. 3, p. 271-290, 2017

Informações sobre o DOI: 10.5351/CSAM.2017.24.3.271 (Fonte: oaDOI API)

A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas

ABNT

CRUZ, José N. da et al. Bivariate odd-log-logistic-Weibull regression model for oral health-related quality of life. Communications for Statistical Applications and Methods, v. 24, n. 3, p. 271-290, 2017Tradução . . Disponível em: https://doi.org/10.5351/CSAM.2017.24.3.271. Acesso em: 30 dez. 2025.
APA

Cruz, J. N. da, Ortega, E. M. M., Cordeiro, G. M., Suzuki, A. K., & Mialhe, F. L. (2017). Bivariate odd-log-logistic-Weibull regression model for oral health-related quality of life. Communications for Statistical Applications and Methods, 24( 3), 271-290. doi:10.5351/CSAM.2017.24.3.271
NLM

Cruz JN da, Ortega EMM, Cordeiro GM, Suzuki AK, Mialhe FL. Bivariate odd-log-logistic-Weibull regression model for oral health-related quality of life [Internet]. Communications for Statistical Applications and Methods. 2017 ; 24( 3): 271-290.[citado 2025 dez. 30 ] Available from: https://doi.org/10.5351/CSAM.2017.24.3.271
Vancouver

Cruz JN da, Ortega EMM, Cordeiro GM, Suzuki AK, Mialhe FL. Bivariate odd-log-logistic-Weibull regression model for oral health-related quality of life [Internet]. Communications for Statistical Applications and Methods. 2017 ; 24( 3): 271-290.[citado 2025 dez. 30 ] Available from: https://doi.org/10.5351/CSAM.2017.24.3.271