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Filtros : "Communications in Statistics - Theory and Methods" "MODELOS NÃO LINEARES" Limpar

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  • Artigo de periodico

    Additive models with p-order autoregressive skew-normal errors for modeling trend and seasonality in time series (2025)

    Ferreira, Clécio S. ; Paula, Gilberto Alvarenga ; Oliveira, Rodrigo Alves de

    Source: Communications in Statistics - Theory and Methods. Unidade: IME

    Subjects: MODELOS NÃO LINEARES, ANÁLISE DE SÉRIES TEMPORAIS

    Versão PublicadaAcesso à fonteDOIHow to cite
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    • ABNT

      FERREIRA, Clécio S. e PAULA, Gilberto Alvarenga e OLIVEIRA, Rodrigo Alves de. Additive models with p-order autoregressive skew-normal errors for modeling trend and seasonality in time series. Communications in Statistics - Theory and Methods, v. 54, n. 18, p. 5703–5725, 2025Tradução . . Disponível em: https://doi.org/10.1080/03610926.2024.2444519. Acesso em: 10 nov. 2025.

    • APA

      Ferreira, C. S., Paula, G. A., & Oliveira, R. A. de. (2025). Additive models with p-order autoregressive skew-normal errors for modeling trend and seasonality in time series. Communications in Statistics - Theory and Methods, 54( 18), 5703–5725. doi:10.1080/03610926.2024.2444519

    • NLM

      Ferreira CS, Paula GA, Oliveira RA de. Additive models with p-order autoregressive skew-normal errors for modeling trend and seasonality in time series [Internet]. Communications in Statistics - Theory and Methods. 2025 ; 54( 18): 5703–5725.[citado 2025 nov. 10 ] Available from: https://doi.org/10.1080/03610926.2024.2444519

    • Vancouver

      Ferreira CS, Paula GA, Oliveira RA de. Additive models with p-order autoregressive skew-normal errors for modeling trend and seasonality in time series [Internet]. Communications in Statistics - Theory and Methods. 2025 ; 54( 18): 5703–5725.[citado 2025 nov. 10 ] Available from: https://doi.org/10.1080/03610926.2024.2444519

  • Artigo de periodico

    Likelihood-based inference for the transmuted log-logistic model in the presence of right-censored data (2019)

    Granzotto, Daniele C. T; Ferreira, Paulo H.; Louzada, Francisco

    Source: Communications in Statistics - Theory and Methods. Unidade: ICMC

    Subjects: INFERÊNCIA BAYESIANA, ANÁLISE DE SOBREVIVÊNCIA, ESTATÍSTICA E PROBABILIDADE, MODELOS NÃO LINEARES, LOGÍSTICA, MODELOS MATEMÁTICOS, DADOS CENSURADOS

    Acesso à fonteDOIHow to cite
    A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas

    • ABNT

      GRANZOTTO, Daniele C. T e FERREIRA, Paulo H. e LOUZADA, Francisco. Likelihood-based inference for the transmuted log-logistic model in the presence of right-censored data. Communications in Statistics - Theory and Methods, v. 48, n. 7, p. 1798-1813, 2019Tradução . . Disponível em: https://doi.org/10.1080/03610926.2018.1440313. Acesso em: 10 nov. 2025.

    • APA

      Granzotto, D. C. T., Ferreira, P. H., & Louzada, F. (2019). Likelihood-based inference for the transmuted log-logistic model in the presence of right-censored data. Communications in Statistics - Theory and Methods, 48( 7), 1798-1813. doi:10.1080/03610926.2018.1440313

    • NLM

      Granzotto DCT, Ferreira PH, Louzada F. Likelihood-based inference for the transmuted log-logistic model in the presence of right-censored data [Internet]. Communications in Statistics - Theory and Methods. 2019 ; 48( 7): 1798-1813.[citado 2025 nov. 10 ] Available from: https://doi.org/10.1080/03610926.2018.1440313

    • Vancouver

      Granzotto DCT, Ferreira PH, Louzada F. Likelihood-based inference for the transmuted log-logistic model in the presence of right-censored data [Internet]. Communications in Statistics - Theory and Methods. 2019 ; 48( 7): 1798-1813.[citado 2025 nov. 10 ] Available from: https://doi.org/10.1080/03610926.2018.1440313
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