Generalization of Log-Logistic Family with Quantile Regression Model

Lak F., ALTUN E., Alizadeh M., Contreras-Reyes J. E., Esmaeili H.

Mathematical and Computational Applications, cilt.31, sa.1, 2026 (ESCI, Scopus) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 31 Sayı: 1
  • Basım Tarihi: 2026
  • Doi Numarası: 10.3390/mca31010007
  • Dergi Adı: Mathematical and Computational Applications
  • Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI), Scopus, INSPEC, zbMATH, Directory of Open Access Journals
  • Anahtar Kelimeler: logistic distribution, maximum likelihood estimation, regression, residuals, simulation
  • Gazi Üniversitesi Adresli: Evet

Özet

A new general class of distributions is proposed by applying the transformation to the random variable that follows the generalized odd-logistic family. Using the proposed family, we introduce a flexible Weibull distribution. The importance of the proposed distribution is demonstrated and compared with different generalizations of the Weibull distribution via three real data applications. A quantile regression model is obtained using the newly developed Weibull model and compared with the standard Weibull quantile regression model through a real data application.