Hierarchical transmuted log-logistic model: a subjective bayesian analysis (2018)
- Authors:
- Autor USP: LOUZADA NETO, FRANCISCO - ICMC
- Unidade: ICMC
- DOI: 10.3390/jrfm11010013
- Subjects: INFERÊNCIA BAYESIANA; MODELOS PARA PROCESSOS ESTOCÁSTICOS; DISTRIBUIÇÕES (PROBABILIDADE); VEROSSIMILHANÇA; REGRESSÃO LOGÍSTICA; ANÁLISE DE SOBREVIVÊNCIA
- Keywords: hierarchical Bayesian model; influential analysis; log-logistic distribution; transmuted map
- Language: Inglês
- Imprenta:
- Publisher place: Basel, Switzerland
- Date published: 2018
- Source:
- Título: Journal of Risk and Financial Management
- ISSN: 1911-8066
- Volume/Número/Paginação/Ano: v. 11, n. 1, p. 1-12, Mar. 2018
- Este periódico é de acesso aberto
- Este artigo é de acesso aberto
- URL de acesso aberto
- Cor do Acesso Aberto: gold
- Licença: cc-by
-
ABNT
SANTOS, Carlos A. dos et al. Hierarchical transmuted log-logistic model: a subjective bayesian analysis. Journal of Risk and Financial Management, v. 11, n. 1, p. 1-12, 2018Tradução . . Disponível em: https://doi.org/10.3390/jrfm11010013. Acesso em: 07 nov. 2024. -
APA
Santos, C. A. dos, Granzotto, D. C. T., Tomazella, V. L. D., & Louzada, F. (2018). Hierarchical transmuted log-logistic model: a subjective bayesian analysis. Journal of Risk and Financial Management, 11( 1), 1-12. doi:10.3390/jrfm11010013 -
NLM
Santos CA dos, Granzotto DCT, Tomazella VLD, Louzada F. Hierarchical transmuted log-logistic model: a subjective bayesian analysis [Internet]. Journal of Risk and Financial Management. 2018 ; 11( 1): 1-12.[citado 2024 nov. 07 ] Available from: https://doi.org/10.3390/jrfm11010013 -
Vancouver
Santos CA dos, Granzotto DCT, Tomazella VLD, Louzada F. Hierarchical transmuted log-logistic model: a subjective bayesian analysis [Internet]. Journal of Risk and Financial Management. 2018 ; 11( 1): 1-12.[citado 2024 nov. 07 ] Available from: https://doi.org/10.3390/jrfm11010013 - Cooperative parallel particle filters for online model selection and applications to urban mobility
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Informações sobre o DOI: 10.3390/jrfm11010013 (Fonte: oaDOI API)
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