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Filtros : "Martinez, Edson Zangiacomi" "Paquistão" Removidos: " FCF004" "FFCLRP-591" Limpar

Filtros

Autores USP
- achcar, jorge alberto (~2)
- martinez, edson zangiacomi (~2)
- peres, marcos vinicius de oliveira (~1)
Ano de publicação
- 2022 (~1)
- 2021 (~1)
Assuntos
- análise de sobrevivência (1)
- dados censurados (1)
- epidemiologia (1)
- estudos ecológicos (1)
- inferência bayesiana (1)

Artigo de periodico
Bayesian bivariate spatial shared component model: mapping breast and cervical cancer mortality in Southern Brazil (2022)
Martinez, Edson Zangiacomi ; Silva, Diego Gafuri; Resende, Larissa Intrebartoli; Lizzi, Elisangela Aparecida da Silva; Achcar, Jorge Alberto
Fonte: Pakistan Journal of Statistics and Operation Research. Unidade: FMRP
Assuntos: ESTUDOS ECOLÓGICOS, EPIDEMIOLOGIA, INFERÊNCIA ESTATÍSTICA, PROCESSOS ESTOCÁSTICOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
MARTINEZ, Edson Zangiacomi et al. Bayesian bivariate spatial shared component model: mapping breast and cervical cancer mortality in Southern Brazil. Pakistan Journal of Statistics and Operation Research, v. 18, n. 4, p. 775-788, 2022Tradução . . Disponível em: https://doi.org/10.18187/pjsor.v18i4.4095. Acesso em: 25 ago. 2024.
APA
Martinez, E. Z., Silva, D. G., Resende, L. I., Lizzi, E. A. da S., & Achcar, J. A. (2022). Bayesian bivariate spatial shared component model: mapping breast and cervical cancer mortality in Southern Brazil. Pakistan Journal of Statistics and Operation Research, 18( 4), 775-788. doi:10.18187/pjsor.v18i4.4095
NLM
Martinez EZ, Silva DG, Resende LI, Lizzi EA da S, Achcar JA. Bayesian bivariate spatial shared component model: mapping breast and cervical cancer mortality in Southern Brazil [Internet]. Pakistan Journal of Statistics and Operation Research. 2022 ; 18( 4): 775-788.[citado 2024 ago. 25 ] Available from: https://doi.org/10.18187/pjsor.v18i4.4095
Vancouver
Martinez EZ, Silva DG, Resende LI, Lizzi EA da S, Achcar JA. Bayesian bivariate spatial shared component model: mapping breast and cervical cancer mortality in Southern Brazil [Internet]. Pakistan Journal of Statistics and Operation Research. 2022 ; 18( 4): 775-788.[citado 2024 ago. 25 ] Available from: https://doi.org/10.18187/pjsor.v18i4.4095
Artigo de periodico
Classical and Bayesian inference approaches for the exponentiated discrete Weibull model with censored data and a cure fraction (2021)
Freitas, Bruno Caparroz Lopes de; Martinez, Edson Zangiacomi ; Achcar, Jorge Alberto ; Peres, Marcos Vinícius de Oliveira
Fonte: Pakistan Journal of Statistics and Operation Research. Unidade: FMRP
Assuntos: ANÁLISE DE SOBREVIVÊNCIA, VEROSSIMILHANÇA, DADOS CENSURADOS, INFERÊNCIA BAYESIANA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
FREITAS, Bruno Caparroz Lopes de et al. Classical and Bayesian inference approaches for the exponentiated discrete Weibull model with censored data and a cure fraction. Pakistan Journal of Statistics and Operation Research, v. 17, n. 2, p. 467-481, 2021Tradução . . Disponível em: https://doi.org/10.18187/pjsor.v17i2.3693. Acesso em: 25 ago. 2024.
APA
Freitas, B. C. L. de, Martinez, E. Z., Achcar, J. A., & Peres, M. V. de O. (2021). Classical and Bayesian inference approaches for the exponentiated discrete Weibull model with censored data and a cure fraction. Pakistan Journal of Statistics and Operation Research, 17( 2), 467-481. doi:10.18187/pjsor.v17i2.3693
NLM
Freitas BCL de, Martinez EZ, Achcar JA, Peres MV de O. Classical and Bayesian inference approaches for the exponentiated discrete Weibull model with censored data and a cure fraction [Internet]. Pakistan Journal of Statistics and Operation Research. 2021 ; 17( 2): 467-481.[citado 2024 ago. 25 ] Available from: https://doi.org/10.18187/pjsor.v17i2.3693
Vancouver
Freitas BCL de, Martinez EZ, Achcar JA, Peres MV de O. Classical and Bayesian inference approaches for the exponentiated discrete Weibull model with censored data and a cure fraction [Internet]. Pakistan Journal of Statistics and Operation Research. 2021 ; 17( 2): 467-481.[citado 2024 ago. 25 ] Available from: https://doi.org/10.18187/pjsor.v17i2.3693

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