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Artigo de periodico
The odd log-logistic Lindley Poisson model for lifetime data (2017)
Ozel, Gamze; Alizadeh, Morad; Cakmakyapan, Selen; Hamedani, G. G.; Ortega, Edwin Moises Marcos; Cancho, Vicente Garibay
Source: Communications in Statistics - Simulation and Computation. Unidades: ESALQ, ICMC
Subjects: ANÁLISE DE SOBREVIVÊNCIA, DADOS CENSURADOS, DISTRIBUIÇÕES (PROBABILIDADE), MODELOS MATEMÁTICOS, VEROSSIMILHANÇA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
OZEL, Gamze et al. The odd log-logistic Lindley Poisson model for lifetime data. Communications in Statistics - Simulation and Computation, v. 46, n. 8, p. 6513-6537, 2017Tradução . . Disponível em: https://doi.org/10.1080/03610918.2016.1206931. Acesso em: 01 nov. 2024.
APA
Ozel, G., Alizadeh, M., Cakmakyapan, S., Hamedani, G. G., Ortega, E. M. M., & Cancho, V. G. (2017). The odd log-logistic Lindley Poisson model for lifetime data. Communications in Statistics - Simulation and Computation, 46( 8), 6513-6537. doi:10.1080/03610918.2016.1206931
NLM
Ozel G, Alizadeh M, Cakmakyapan S, Hamedani GG, Ortega EMM, Cancho VG. The odd log-logistic Lindley Poisson model for lifetime data [Internet]. Communications in Statistics - Simulation and Computation. 2017 ; 46( 8): 6513-6537.[citado 2024 nov. 01 ] Available from: https://doi.org/10.1080/03610918.2016.1206931
Vancouver
Ozel G, Alizadeh M, Cakmakyapan S, Hamedani GG, Ortega EMM, Cancho VG. The odd log-logistic Lindley Poisson model for lifetime data [Internet]. Communications in Statistics - Simulation and Computation. 2017 ; 46( 8): 6513-6537.[citado 2024 nov. 01 ] Available from: https://doi.org/10.1080/03610918.2016.1206931
Artigo de periodico
The new family of distributions and applications in heteroscedastic regression analysis (2017)
Cordeiro, Gauss M; Ramires, Thiago G; Ortega, Edwin Moises Marcos; Alizadeh, Morad
Source: Journal of Statistical Theory and Applications. Unidade: ESALQ
Subjects: ANÁLISE DE REGRESSÃO E DE CORRELAÇÃO, DADOS CENSURADOS, DISTRIBUIÇÕES (PROBABILIDADE), HETEROSCEDASTICIDADE, MÉTODO DE CAUCHY, VEROSSIMILHANÇA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
CORDEIRO, Gauss M et al. The new family of distributions and applications in heteroscedastic regression analysis. Journal of Statistical Theory and Applications, v. 16, n. 3, p. 401-418, 2017Tradução . . Disponível em: https://doi.org/10.2991/jsta.2017.16.3.11. Acesso em: 01 nov. 2024.
APA
Cordeiro, G. M., Ramires, T. G., Ortega, E. M. M., & Alizadeh, M. (2017). The new family of distributions and applications in heteroscedastic regression analysis. Journal of Statistical Theory and Applications, 16( 3), 401-418. doi:10.2991/jsta.2017.16.3.11
NLM
Cordeiro GM, Ramires TG, Ortega EMM, Alizadeh M. The new family of distributions and applications in heteroscedastic regression analysis [Internet]. Journal of Statistical Theory and Applications. 2017 ; 16( 3): 401-418.[citado 2024 nov. 01 ] Available from: https://doi.org/10.2991/jsta.2017.16.3.11
Vancouver
Cordeiro GM, Ramires TG, Ortega EMM, Alizadeh M. The new family of distributions and applications in heteroscedastic regression analysis [Internet]. Journal of Statistical Theory and Applications. 2017 ; 16( 3): 401-418.[citado 2024 nov. 01 ] Available from: https://doi.org/10.2991/jsta.2017.16.3.11
Artigo de periodico
The Log-gamma-logistic regression model: estimation, sensibility and residual analysis (2017)
Hashimoto, Elizabeth M; Edwin Moises Marcos Ortega ; Cordeiro, Gauss M; Hamedani, G. G
Source: Journal of Statistical Theory and Applications. Unidade: ESALQ
Subjects: DADOS CENSURADOS, DISTRIBUIÇÃO LOGÍSTICA, MODELOS MATEMÁTICOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
HASHIMOTO, Elizabeth M et al. The Log-gamma-logistic regression model: estimation, sensibility and residual analysis. Journal of Statistical Theory and Applications, v. 16, n. 4, p. 547-564, 2017Tradução . . Disponível em: https://www.atlantis-press.com/journals/jsta/25887941. Acesso em: 01 nov. 2024.
APA
Hashimoto, E. M., Edwin Moises Marcos Ortega,, Cordeiro, G. M., & Hamedani, G. G. (2017). The Log-gamma-logistic regression model: estimation, sensibility and residual analysis. Journal of Statistical Theory and Applications, 16( 4), 547-564. Recuperado de https://www.atlantis-press.com/journals/jsta/25887941
NLM
Hashimoto EM, Edwin Moises Marcos Ortega, Cordeiro GM, Hamedani GG. The Log-gamma-logistic regression model: estimation, sensibility and residual analysis [Internet]. Journal of Statistical Theory and Applications. 2017 ; 16( 4): 547-564.[citado 2024 nov. 01 ] Available from: https://www.atlantis-press.com/journals/jsta/25887941
Vancouver
Hashimoto EM, Edwin Moises Marcos Ortega, Cordeiro GM, Hamedani GG. The Log-gamma-logistic regression model: estimation, sensibility and residual analysis [Internet]. Journal of Statistical Theory and Applications. 2017 ; 16( 4): 547-564.[citado 2024 nov. 01 ] Available from: https://www.atlantis-press.com/journals/jsta/25887941
Artigo de periodico
The odd log-logistic generalized gamma model: properties, applications, classical and bayesian approach (2017)
Prataviera, Fábio; Cordeiro, Gauss M.; Suzuki, Adriano Kamimura ; Ortega, Edwin Moisés Marcos
Source: Biometrics and Biostatistics International Journal. Unidades: ICMC, ESALQ
Subjects: ANÁLISE DE SOBREVIVÊNCIA, DADOS CENSURADOS, DISTRIBUIÇÕES (PROBABILIDADE), INFERÊNCIA BAYESIANA, MODELOS MATEMÁTICOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
PRATAVIERA, Fábio et al. The odd log-logistic generalized gamma model: properties, applications, classical and bayesian approach. Biometrics and Biostatistics International Journal, v. 6, n. 4, p. 1-19, 2017Tradução . . Disponível em: https://doi.org/10.15406/bbij.2017.06.00174. Acesso em: 01 nov. 2024.
APA
Prataviera, F., Cordeiro, G. M., Suzuki, A. K., & Ortega, E. M. M. (2017). The odd log-logistic generalized gamma model: properties, applications, classical and bayesian approach. Biometrics and Biostatistics International Journal, 6( 4), 1-19. doi:10.15406/bbij.2017.06.00174
NLM
Prataviera F, Cordeiro GM, Suzuki AK, Ortega EMM. The odd log-logistic generalized gamma model: properties, applications, classical and bayesian approach [Internet]. Biometrics and Biostatistics International Journal. 2017 ; 6( 4): 1-19.[citado 2024 nov. 01 ] Available from: https://doi.org/10.15406/bbij.2017.06.00174
Vancouver
Prataviera F, Cordeiro GM, Suzuki AK, Ortega EMM. The odd log-logistic generalized gamma model: properties, applications, classical and bayesian approach [Internet]. Biometrics and Biostatistics International Journal. 2017 ; 6( 4): 1-19.[citado 2024 nov. 01 ] Available from: https://doi.org/10.15406/bbij.2017.06.00174
Artigo de periodico
The Log-gamma-logistic regression model: estimation, sensibility and residual analysis (2017)
Hashimoto, Elizabeth M; Ortega, Edwin Moises Marcos ; Cordeiro, Gauss M; Hamedani, G.G
Source: Journal of Statistical Theory and Applications. Unidade: ESALQ
Subjects: DADOS CENSURADOS, DISTRIBUIÇÃO LOGÍSTICA, MODELOS MATEMÁTICOS, VEROSSIMILHANÇA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
HASHIMOTO, Elizabeth M et al. The Log-gamma-logistic regression model: estimation, sensibility and residual analysis. Journal of Statistical Theory and Applications, v. 16, n. 4, p. 547–564, 2017Tradução . . Disponível em: https://doi.org/10.2991/jsta.2017.16.4.9. Acesso em: 01 nov. 2024.
APA
Hashimoto, E. M., Ortega, E. M. M., Cordeiro, G. M., & Hamedani, G. G. (2017). The Log-gamma-logistic regression model: estimation, sensibility and residual analysis. Journal of Statistical Theory and Applications, 16( 4), 547–564. doi:10.2991/jsta.2017.16.4.9
NLM
Hashimoto EM, Ortega EMM, Cordeiro GM, Hamedani GG. The Log-gamma-logistic regression model: estimation, sensibility and residual analysis [Internet]. Journal of Statistical Theory and Applications. 2017 ; 16( 4): 547–564.[citado 2024 nov. 01 ] Available from: https://doi.org/10.2991/jsta.2017.16.4.9
Vancouver
Hashimoto EM, Ortega EMM, Cordeiro GM, Hamedani GG. The Log-gamma-logistic regression model: estimation, sensibility and residual analysis [Internet]. Journal of Statistical Theory and Applications. 2017 ; 16( 4): 547–564.[citado 2024 nov. 01 ] Available from: https://doi.org/10.2991/jsta.2017.16.4.9

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