Transmuted Logistic-Exponential Distribution for Modelling Lifetime Data


  • Ogunsola I. Adesegun Department of Statistics, College of Physical Sciences, Federal University of Agriculture, Abeokuta, Nigeria
  • Dawodu G. Abayomi Department of Statistics, College of Physical Sciences, Federal University of Agriculture, Abeokuta, Nigeria
  • Soyinka A. Taiwo Department of Statistics, College of Physical Sciences, Federal University of Agriculture, Abeokuta, Nigeria
  • Osinuga I. Ademola Department of Mathematics, College of Physical Sciences, Federal University of Agriculture, Abeokuta, Nigeria
  • Adetona B. Omotola Department of Statistics, College of Physical Sciences, Federal University of Agriculture, Abeokuta, Nigeria


Failure rate, skewed data, quadratic rank and transmutation map


Logistic-exponential (LE) distribution is one of the rare distributions in existence for modeling lifetime data due to its unique features. It is the only two-parameter distribution with quintuplet characteristics of hazard failure rates. However, its limitation is inability to model extremely skewed real life situation phenomena appropriately. This study proposed, developed and studied a new transmuted logistic-exponential distribution with three parameters with the aim of increasing the shape flexibility of LE distribution that will be more applicable to skewed lifetime data in various fields. We adopt the cumulative distribution function of LE and the quadratic rank transmutation map (QRTM) function in its development. Its quantile, survival, hazard functions, order statistics, skewness and kurtosis were derived. Its hazard function was found to have increasing, decreasing and constant failure rates while the survival function has a decreasing shape. Again, the estimates of the parameters were obtained using maximum likelihood estimation technique. Its efficiency was examined using real life dataset. The maximum likelihood estimates obtained were compared with the existing similar distributions using Akaike information criteria (AIC) and log-likelihood. The result showed that the newly transmuted LE distribution outperformed other models fitted to the dataset. The fitness of the distributions was also examined using two goodness of fit test. Both Kolmogorov-Smirnov and Anderson-Darling tests statistics confirmed that NTLE has a good fit. Hence, the new transmuted logistic-exponential (NTLE) distribution is more appropriate in modeling skewed lifetime datasets. In future research, we intend to study some other properties of this newly transmuted distribution and compare different estimation procedures for its parameters.


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How to Cite

I. Adesegun, O. ., G. Abayomi, D. ., A. Taiwo, S. ., I. Ademola, O. ., & B. Omotola, A. . (2023). Transmuted Logistic-Exponential Distribution for Modelling Lifetime Data. Thailand Statistician, 21(4), 943–960. Retrieved from