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
Fonte: Journal of Statistical Theory and Applications. Unidade: ESALQ
Assuntos: DADOS CENSURADOS, DISTRIBUIÇÃO LOGÍSTICA, MODELOS MATEMÁTICOS, VEROSSIMILHANÇA
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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: 13 nov. 2025.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.2017.16.4.9NLM
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 2025 nov. 13 ] Available from: https://doi.org/10.2991/jsta.2017.16.4.9Vancouver
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 2025 nov. 13 ] Available from: https://doi.org/10.2991/jsta.2017.16.4.9
