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
Fonte: Journal of Statistical Theory and Applications. Unidade: ESALQ
Assuntos: DADOS CENSURADOS, DISTRIBUIÇÃO LOGÍSTICA, MODELOS MATEMÁTICOS
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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://www.atlantis-press.com/journals/jsta/25887941. Acesso em: 11 nov. 2025.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/25887941NLM
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 2025 nov. 11 ] Available from: https://www.atlantis-press.com/journals/jsta/25887941Vancouver
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 2025 nov. 11 ] Available from: https://www.atlantis-press.com/journals/jsta/25887941
