Volume 19, Issue 4, December 2020, Pages 487 - 505
Transmuted Kumaraswamy Weibull Distribution with Covariates Regression Modelling to Analyze Reliability Data
Authors
Muhammad Shuaib Khan1, *, Robert King1, Irene Lena Hudson2
1School of Mathematical and Physical Sciences, The University of Newcastle, Callaghan, NSW, 2308, Australia
2Department of Mathematical Sciences, Royal Melbourne Institute of Technology (RMIT), Melbourne, VIC, Australia
*Corresponding author. Email: muhammad.s.khan@newcastle.edu.au
Corresponding Author
Muhammad Shuaib Khan
Received 4 May 2020, Accepted 12 October 2020, Available Online 29 October 2020.
- DOI
- 10.2991/jsta.d.201016.003How to use a DOI?
- Keywords
- Log-Kumaraswamy Weibull regression model; Moments; Maximum likelihood estimation
- Abstract
This paper investigates the potential usefulness of the transmuted Kumaraswamy Weibull distribution by using quadratic rank transmutation map technique for modelling reliability data. Some structural properties of the transmuted Kumaraswamy Weibull distribution are discussed. We propose a location-scale regression model based on the transmuted log-Kumaraswamy Weibull distribution for modelling survival data. We discuss estimation of the model parameters by the method of maximum likelihood and provide two applications to illustrate the potentiality of the proposed family of lifetime distributions.
- Copyright
- © 2020 The Authors. Published by Atlantis Press B.V.
- Open Access
- This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).
Download article (PDF)
View full text (HTML)
Cite this article
TY - JOUR AU - Muhammad Shuaib Khan AU - Robert King AU - Irene Lena Hudson PY - 2020 DA - 2020/10/29 TI - Transmuted Kumaraswamy Weibull Distribution with Covariates Regression Modelling to Analyze Reliability Data JO - Journal of Statistical Theory and Applications SP - 487 EP - 505 VL - 19 IS - 4 SN - 2214-1766 UR - https://doi.org/10.2991/jsta.d.201016.003 DO - 10.2991/jsta.d.201016.003 ID - Khan2020 ER -