Document Type
Article
Source Publication Title
Applied Mathematical Sciences
First Page
311
Last Page
329
DOI
https://doi.org/10.12988/ams.2017.612283
Abstract
The main purpose of this paper is to characterize the log-logistic (LL) distributions through the methods of percentiles and L-moments and contrast with the method of (product) moments. The method of (product) moments (MoM) has certain limitations when compared with method of percentiles (MoP) and method of L-moments (MoLM) in the context of fitting empirical and theoretical distributions and estimation of parameters, especially when distributions with greater departure from normality are involved. Systems of equations based on MoP and MoLM are derived. A methodology to simulate univariate LL distributions based on each of the two methods (MoP and MoLM) is developed and contrasted with MoM in terms of fitting distributions and estimation of parameters. Monte Carlo simulation results indicate that the MoPand MoLM-based LL distributions are superior to their MoM based counterparts in the context of fitting distributions and estimation of parameters.
Disciplines
Curriculum and Instruction | Education
Publication Date
1-25-2017
Language
English
License
This work is licensed under a Creative Commons Attribution 3.0 License.
Recommended Citation
Pant, Mohan, "Characterizing Log-Logistic (LL) Distributions through Methods of Percentiles and L-Moments" (2017). Curriculum and Instruction Faculty Publications. 71.
https://mavmatrix.uta.edu/curriculuminstruction_facpubs/71