A New Generalized Class of Distributions: Properties and Estimation Based on Type-I Censored Samples

Zubair Ahmad ()
Additional contact information
Zubair Ahmad: Quaid-i-Azam University 45320

Annals of Data Science, 2020, vol. 7, issue 2, No 3, 243-256

Abstract: Abstract This article introduces a new generalized family of distributions, which is a generalization of the exponentiated and transmuted family of distributions. A special model of this family, namely, new generalized Weibull distribution is considered in detail. General expressions for the mathematical properties of the proposed family are derived. Maximum likelihood estimates of the unknown parameters are obtained. A simulation study is done to evaluate the performances of the maximum likelihood estimators. Furthermore, estimation based on Type-I censored samples is also discussed. Finally, the superiority of the new proposal is illustrated empirically by analyzing a real-life application.

Keywords: Exponentiated family; Transmuted family; Weibull distribution; Type-I censoring scheme; Order statistics; Maximum likelihood estimation (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s40745-018-0160-5 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_s40745-018-0160-5

Ordering information: This journal article can be ordered from
https://www.springer ... gement/journal/40745

DOI: 10.1007/s40745-018-0160-5

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 ().