The Zubair-G Family of Distributions: Properties and Applications
Zubair Ahmad ()
Additional contact information
Zubair Ahmad: Quaid-i-Azam University 45320
Annals of Data Science, 2020, vol. 7, issue 2, No 1, 195-208
Abstract:
Abstract In this article, a new method is suggested to expand a family of life distributions by adding an additional parameter. The new proposal may be named as the Zubair-G family of distributions. For this family, general expressions for some mathematical properties are derived. The maximum product spacing, ordinary least square and maximum likelihood methods are discussed to estimate the model parameters. A three-parameter special sub-model of the proposed family, called the Zubair–Weibull distribution is considered in detail. Its density function can be symmetrical, left-skewed, right-skewed, and has increasing, decreasing, bathtub and upside-down bathtub shaped failure rates. To illustrate the importance of the proposed family over the other well-known methods, two applications to real data sets are analyzed.
Keywords: Family of distributions; Weibull distribution; Moments; Order statistic; Residual life function; Estimation (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)
Downloads: (external link)
http://link.springer.com/10.1007/s40745-018-0169-9 Abstract (text/html)
Access to the full text of the articles in this series is restricted.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:spr:aodasc:v:7:y:2020:i:2:d:10.1007_s40745-018-0169-9
Ordering information: This journal article can be ordered from
https://www.springer ... gement/journal/40745
DOI: 10.1007/s40745-018-0169-9
Access Statistics for this article
Annals of Data Science is currently edited by Yong Shi
More articles in Annals of Data Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().