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Artigo de periodico
Zero-spiked regression models generated by gamma random variables with application in the resin oil production (2019)
Hashimoto, Elizabeth M; Ortega, Edwin Moises Marcos; Cordeiro, Gauss M; Cancho, Vicente Garibay ; Klauberg, Carine
Fonte: Journal of Statistical Computation and Simulation. Unidade: ESALQ
Assuntos: REGRESSÃO LINEAR, ÓLEOS VEGETAIS, COPAÍBA, RESINAS VEGETAIS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
HASHIMOTO, Elizabeth M et al. Zero-spiked regression models generated by gamma random variables with application in the resin oil production. Journal of Statistical Computation and Simulation, v. 89, n. 1, p. 52-70, 2019Tradução . . Disponível em: https://doi.org/10.1080/00949655.2018.1534116. Acesso em: 20 nov. 2025.
APA
Hashimoto, E. M., Ortega, E. M. M., Cordeiro, G. M., Cancho, V. G., & Klauberg, C. (2019). Zero-spiked regression models generated by gamma random variables with application in the resin oil production. Journal of Statistical Computation and Simulation, 89( 1), 52-70. doi:10.1080/00949655.2018.1534116
NLM
Hashimoto EM, Ortega EMM, Cordeiro GM, Cancho VG, Klauberg C. Zero-spiked regression models generated by gamma random variables with application in the resin oil production [Internet]. Journal of Statistical Computation and Simulation. 2019 ; 89( 1): 52-70.[citado 2025 nov. 20 ] Available from: https://doi.org/10.1080/00949655.2018.1534116
Vancouver
Hashimoto EM, Ortega EMM, Cordeiro GM, Cancho VG, Klauberg C. Zero-spiked regression models generated by gamma random variables with application in the resin oil production [Internet]. Journal of Statistical Computation and Simulation. 2019 ; 89( 1): 52-70.[citado 2025 nov. 20 ] Available from: https://doi.org/10.1080/00949655.2018.1534116
Artigo de periodico
A data-driven selection of the number of clusters in the Dirichlet allocation model via Bayesian mixture modelling (2019)
Saraiva, Erlandson Ferreira ; Pereira, C. A. B; Suzuki, Adriano Kamimura
Fonte: Journal of Statistical Computation and Simulation. Unidade: ICMC
Assuntos: CLUSTERS, ALGORITMOS ÚTEIS E ESPECÍFICOS, DISTRIBUIÇÕES (PROBABILIDADE)
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SARAIVA, Erlandson Ferreira e PEREIRA, C. A. B e SUZUKI, Adriano Kamimura. A data-driven selection of the number of clusters in the Dirichlet allocation model via Bayesian mixture modelling. Journal of Statistical Computation and Simulation, v. 89, n. 15, p. 2848-2870, 2019Tradução . . Disponível em: https://doi.org/10.1080/00949655.2019.1643345. Acesso em: 20 nov. 2025.
APA
Saraiva, E. F., Pereira, C. A. B., & Suzuki, A. K. (2019). A data-driven selection of the number of clusters in the Dirichlet allocation model via Bayesian mixture modelling. Journal of Statistical Computation and Simulation, 89( 15), 2848-2870. doi:10.1080/00949655.2019.1643345
NLM
Saraiva EF, Pereira CAB, Suzuki AK. A data-driven selection of the number of clusters in the Dirichlet allocation model via Bayesian mixture modelling [Internet]. Journal of Statistical Computation and Simulation. 2019 ; 89( 15): 2848-2870.[citado 2025 nov. 20 ] Available from: https://doi.org/10.1080/00949655.2019.1643345
Vancouver
Saraiva EF, Pereira CAB, Suzuki AK. A data-driven selection of the number of clusters in the Dirichlet allocation model via Bayesian mixture modelling [Internet]. Journal of Statistical Computation and Simulation. 2019 ; 89( 15): 2848-2870.[citado 2025 nov. 20 ] Available from: https://doi.org/10.1080/00949655.2019.1643345
Artigo de periodico
Likelihood-based tests in zero-inflated power series models (2019)
Zavaleta, Katherine Elizabeth Coaguila; Cancho, Vicente Garibay ; Lemonte, Artur José
Fonte: Journal of Statistical Computation and Simulation. Unidade: ICMC
Assuntos: INFERÊNCIA PARAMÉTRICA, INFERÊNCIA BAYESIANA, MÉTODO DE MONTE CARLO, VEROSSIMILHANÇA, TESTES DE HIPÓTESES
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
ZAVALETA, Katherine Elizabeth Coaguila e CANCHO, Vicente Garibay e LEMONTE, Artur José. Likelihood-based tests in zero-inflated power series models. Journal of Statistical Computation and Simulation, v. 89, n. 3, p. 443-460, 2019Tradução . . Disponível em: https://doi.org/10.1080/00949655.2018.1554660. Acesso em: 20 nov. 2025.
APA
Zavaleta, K. E. C., Cancho, V. G., & Lemonte, A. J. (2019). Likelihood-based tests in zero-inflated power series models. Journal of Statistical Computation and Simulation, 89( 3), 443-460. doi:10.1080/00949655.2018.1554660
NLM
Zavaleta KEC, Cancho VG, Lemonte AJ. Likelihood-based tests in zero-inflated power series models [Internet]. Journal of Statistical Computation and Simulation. 2019 ; 89( 3): 443-460.[citado 2025 nov. 20 ] Available from: https://doi.org/10.1080/00949655.2018.1554660
Vancouver
Zavaleta KEC, Cancho VG, Lemonte AJ. Likelihood-based tests in zero-inflated power series models [Internet]. Journal of Statistical Computation and Simulation. 2019 ; 89( 3): 443-460.[citado 2025 nov. 20 ] Available from: https://doi.org/10.1080/00949655.2018.1554660
Artigo de periodico
Generalized autoregressive and moving average models: multicollinearity, interpretation and a new modified model (2019)
Esparza Albarracin, Orlando Yesid ; Alencar, Airlane Pereira ; Ho, Linda Lee
Fonte: Journal of Statistical Computation and Simulation. Unidades: IME, EP
Assuntos: ANALISE DE SÉRIES TEMPORAIS, ANALISE DE REGRESSÃO E DE CORRELAÇÃO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
ESPARZA ALBARRACIN, Orlando Yesid e ALENCAR, Airlane Pereira e HO, Linda Lee. Generalized autoregressive and moving average models: multicollinearity, interpretation and a new modified model. Journal of Statistical Computation and Simulation, v. 89, n. 10, p. 1819-1840, 2019Tradução . . Disponível em: https://doi.org/10.1080/00949655.2019.1599892. Acesso em: 20 nov. 2025.
APA
Esparza Albarracin, O. Y., Alencar, A. P., & Ho, L. L. (2019). Generalized autoregressive and moving average models: multicollinearity, interpretation and a new modified model. Journal of Statistical Computation and Simulation, 89( 10), 1819-1840. doi:10.1080/00949655.2019.1599892
NLM
Esparza Albarracin OY, Alencar AP, Ho LL. Generalized autoregressive and moving average models: multicollinearity, interpretation and a new modified model [Internet]. Journal of Statistical Computation and Simulation. 2019 ; 89( 10): 1819-1840.[citado 2025 nov. 20 ] Available from: https://doi.org/10.1080/00949655.2019.1599892
Vancouver
Esparza Albarracin OY, Alencar AP, Ho LL. Generalized autoregressive and moving average models: multicollinearity, interpretation and a new modified model [Internet]. Journal of Statistical Computation and Simulation. 2019 ; 89( 10): 1819-1840.[citado 2025 nov. 20 ] Available from: https://doi.org/10.1080/00949655.2019.1599892
Artigo de periodico
Zero-spiked regression models generated by gamma random variables with application in the resin oil production (2019)
Hashimoto, Elizabeth Mie; Ortega, Edwin Moisés Marcos; Cordeiro, Gauss Moutinho; Cancho, Vicente Garibay ; Klauberg, Carine
Fonte: Journal of Statistical Computation and Simulation. Unidades: ESALQ, ICMC
Assuntos: INFERÊNCIA ESTATÍSTICA, ANÁLISE DE REGRESSÃO E DE CORRELAÇÃO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
HASHIMOTO, Elizabeth Mie et al. Zero-spiked regression models generated by gamma random variables with application in the resin oil production. Journal of Statistical Computation and Simulation, v. 89, n. 1, p. 52-70, 2019Tradução . . Disponível em: https://doi.org/10.1080/00949655.2018.1534116. Acesso em: 20 nov. 2025.
APA
Hashimoto, E. M., Ortega, E. M. M., Cordeiro, G. M., Cancho, V. G., & Klauberg, C. (2019). Zero-spiked regression models generated by gamma random variables with application in the resin oil production. Journal of Statistical Computation and Simulation, 89( 1), 52-70. doi:10.1080/00949655.2018.1534116
NLM
Hashimoto EM, Ortega EMM, Cordeiro GM, Cancho VG, Klauberg C. Zero-spiked regression models generated by gamma random variables with application in the resin oil production [Internet]. Journal of Statistical Computation and Simulation. 2019 ; 89( 1): 52-70.[citado 2025 nov. 20 ] Available from: https://doi.org/10.1080/00949655.2018.1534116
Vancouver
Hashimoto EM, Ortega EMM, Cordeiro GM, Cancho VG, Klauberg C. Zero-spiked regression models generated by gamma random variables with application in the resin oil production [Internet]. Journal of Statistical Computation and Simulation. 2019 ; 89( 1): 52-70.[citado 2025 nov. 20 ] Available from: https://doi.org/10.1080/00949655.2018.1534116
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
Fonte: Journal of Statistical Computation and Simulation. Unidade: ICMC
Assuntos: DADOS DE CONTAGEM, TESTES DE HIPÓTESES, DISTRIBUIÇÃO DE POISSON, VEROSSIMILHANÇA, MÉTODO DE MONTE CARLO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
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: 20 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. 20 ] 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. 20 ] Available from: https://doi.org/10.1080/00949655.2019.1572144

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