Journal of Data Science logo


Login Register

  1. Home
  2. Issues
  3. Volume 17, Issue 2 (2019)
  4. A New Three-Parameter Paralogistic Distr ...

Journal of Data Science

Submit your article Information
  • Article info
  • Related articles
  • More
    Article info Related articles

A New Three-Parameter Paralogistic Distribution: Its Properties and Application
Volume 17, Issue 2 (2019), pp. 239–258
R. Idemudia   N. Ekhosuehi  

Authors

 
Placeholder
https://doi.org/10.6339/JDS.201904_17(2).0001
Pub. online: 4 August 2022      Type: Research Article      Open accessOpen Access

Published
4 August 2022

Abstract

In this paper, we considered a new generalization of the paralogistic distribution which we called the three-parameter paralogistic distribution. Some properties of the new distribution which includes the survival function, hazard function, quantile function, moments, Renyi entropy and the maximum likelihood estimation (MLE) of its parameters are obtained. A simulation study shows that the MLE of the parameters of the new distribution is consistent and asymptotically unbiased. An applicability of the new three-parameter paralogistic distribution was subject to a real lifetime data set alongside with some related existing distributions such as the Paralogistic, Gamma, Transformed Beta, Log-logistic and Inverse paralogistic distributions. The results obtained show that the new three-parameter paralogistic distribution was superior to other aforementioned distributions in terms of the Akaike information criterion (AIC) and K-S Statistic values. This claim was further supported by investigating the density plots, P-P plots and Q-Q plots of the distributions for the data set under study.

Related articles PDF XML
Related articles PDF XML

Copyright
No copyright data available.

Keywords
Survival function Hazard function Quantile function

Metrics
since February 2021
960

Article info
views

676

PDF
downloads

Export citation

Copy and paste formatted citation
Placeholder

Download citation in file


Share


RSS

Journal of data science

  • Online ISSN: 1683-8602
  • Print ISSN: 1680-743X

About

  • About journal

For contributors

  • Submit
  • OA Policy
  • Become a Peer-reviewer

Contact us

  • JDS@ruc.edu.cn
  • No. 59 Zhongguancun Street, Haidian District Beijing, 100872, P.R. China
Powered by PubliMill  •  Privacy policy