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, vol.31, no.1, 2026 (ESCI, Scopus) identifier identifier

  • Publication Type: Article / Article
  • Volume: 31 Issue: 1
  • Publication Date: 2026
  • Doi Number: 10.3390/mca31010007
  • Journal Name: Mathematical and Computational Applications
  • Journal Indexes: Emerging Sources Citation Index (ESCI), Scopus, INSPEC, zbMATH, Directory of Open Access Journals
  • Keywords: logistic distribution, maximum likelihood estimation, regression, residuals, simulation
  • Gazi University Affiliated: Yes

Abstract

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.