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Filtros : "Journal of Statistical Computation and Simulation" "Universidade Federal de São Carlos" Limpar

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  • Artigo de periodico

    Frailty model for multiple repairable systems hierarchically represented subject to competing risks (2024)

    Gonzatto Junior, Oilson Alberto ; Fernandes, Willian Rafael; Ramos, Pedro Luiz ; Tomazella, Vera Lucia Damasceno ; Louzada, Francisco

    Source: Journal of Statistical Computation and Simulation. Unidade: ICMC

    Subjects: ESTATÍSTICA APLICADA, TEORIA DA CONFIABILIDADE, RISCO

    PrivadoAcesso à fonteDOIHow to cite
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    • ABNT

      GONZATTO JUNIOR, Oilson Alberto et al. Frailty model for multiple repairable systems hierarchically represented subject to competing risks. Journal of Statistical Computation and Simulation, v. 94, n. 15, p. 3271-3291, 2024Tradução . . Disponível em: https://doi.org/10.1080/00949655.2024.2381515. Acesso em: 12 nov. 2025.

    • APA

      Gonzatto Junior, O. A., Fernandes, W. R., Ramos, P. L., Tomazella, V. L. D., & Louzada, F. (2024). Frailty model for multiple repairable systems hierarchically represented subject to competing risks. Journal of Statistical Computation and Simulation, 94( 15), 3271-3291. doi:10.1080/00949655.2024.2381515

    • NLM

      Gonzatto Junior OA, Fernandes WR, Ramos PL, Tomazella VLD, Louzada F. Frailty model for multiple repairable systems hierarchically represented subject to competing risks [Internet]. Journal of Statistical Computation and Simulation. 2024 ; 94( 15): 3271-3291.[citado 2025 nov. 12 ] Available from: https://doi.org/10.1080/00949655.2024.2381515

    • Vancouver

      Gonzatto Junior OA, Fernandes WR, Ramos PL, Tomazella VLD, Louzada F. Frailty model for multiple repairable systems hierarchically represented subject to competing risks [Internet]. Journal of Statistical Computation and Simulation. 2024 ; 94( 15): 3271-3291.[citado 2025 nov. 12 ] Available from: https://doi.org/10.1080/00949655.2024.2381515

  • Artigo de periodico

    Bayesian analysis of the inverse generalized gamma distribution using objective priors (2021)

    Ramos, Pedro Luiz ; Mota, Alex Leal ; Ferreira, Paulo Henrique ; Ramos, Eduardo ; Tomazella, Vera Lucia Damasceno ; Louzada, Francisco

    Source: Journal of Statistical Computation and Simulation. Unidade: ICMC

    Subjects: ANÁLISE DE SOBREVIVÊNCIA, VEROSSIMILHANÇA, INFERÊNCIA BAYESIANA

    Versão AceitaAcesso à fonteDOIHow to cite
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    • ABNT

      RAMOS, Pedro Luiz et al. Bayesian analysis of the inverse generalized gamma distribution using objective priors. Journal of Statistical Computation and Simulation, v. 91, n. 4, p. 786-816, 2021Tradução . . Disponível em: https://doi.org/10.1080/00949655.2020.1830991. Acesso em: 12 nov. 2025.

    • APA

      Ramos, P. L., Mota, A. L., Ferreira, P. H., Ramos, E., Tomazella, V. L. D., & Louzada, F. (2021). Bayesian analysis of the inverse generalized gamma distribution using objective priors. Journal of Statistical Computation and Simulation, 91( 4), 786-816. doi:10.1080/00949655.2020.1830991

    • NLM

      Ramos PL, Mota AL, Ferreira PH, Ramos E, Tomazella VLD, Louzada F. Bayesian analysis of the inverse generalized gamma distribution using objective priors [Internet]. Journal of Statistical Computation and Simulation. 2021 ; 91( 4): 786-816.[citado 2025 nov. 12 ] Available from: https://doi.org/10.1080/00949655.2020.1830991

    • Vancouver

      Ramos PL, Mota AL, Ferreira PH, Ramos E, Tomazella VLD, Louzada F. Bayesian analysis of the inverse generalized gamma distribution using objective priors [Internet]. Journal of Statistical Computation and Simulation. 2021 ; 91( 4): 786-816.[citado 2025 nov. 12 ] Available from: https://doi.org/10.1080/00949655.2020.1830991

  • Artigo de periodico

    Hypothesis testing for the dispersion parameter of the hyper-Poisson regression model (2019)

    Santos, Daiane de Souza ; Cancho, Vicente Garibay ; Rodrigues, Josemar

    Source: Journal of Statistical Computation and Simulation. Unidade: ICMC

    Subjects: DADOS DE CONTAGEM, TESTES DE HIPÓTESES, DISTRIBUIÇÃO DE POISSON, VEROSSIMILHANÇA, MÉTODO DE MONTE CARLO

    Acesso à fonteDOIHow to cite
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    • ABNT

      SANTOS, Daiane de Souza e CANCHO, Vicente Garibay e RODRIGUES, Josemar. Hypothesis testing for the dispersion parameter of the hyper-Poisson regression model. Journal of Statistical Computation and Simulation, v. 89, n. 5, p. 763-775, 2019Tradução . . Disponível em: https://doi.org/10.1080/00949655.2019.1572144. Acesso em: 12 nov. 2025.

    • APA

      Santos, D. de S., Cancho, V. G., & Rodrigues, J. (2019). Hypothesis testing for the dispersion parameter of the hyper-Poisson regression model. Journal of Statistical Computation and Simulation, 89( 5), 763-775. doi:10.1080/00949655.2019.1572144

    • NLM

      Santos D de S, Cancho VG, Rodrigues J. Hypothesis testing for the dispersion parameter of the hyper-Poisson regression model [Internet]. Journal of Statistical Computation and Simulation. 2019 ; 89( 5): 763-775.[citado 2025 nov. 12 ] Available from: https://doi.org/10.1080/00949655.2019.1572144

    • Vancouver

      Santos D de S, Cancho VG, Rodrigues J. Hypothesis testing for the dispersion parameter of the hyper-Poisson regression model [Internet]. Journal of Statistical Computation and Simulation. 2019 ; 89( 5): 763-775.[citado 2025 nov. 12 ] Available from: https://doi.org/10.1080/00949655.2019.1572144
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