Omega $${{\omega}}$$ ω —Type Probability Models: A Parametric Modification of Probability Distributions

Udochukwu Victor Echebiri (), Nosakhare Liberty Osawe and Chukwuemeka Thomas Onyia
Additional contact information
Udochukwu Victor Echebiri: University of Benin
Nosakhare Liberty Osawe: University of Benin
Chukwuemeka Thomas Onyia: Enugu State University of Science and Technology

Annals of Data Science, 2025, vol. 12, issue 3, No 2, 855-876

Abstract: Abstract A mathematical approach to developing new distributions is reviewed. The method which composes of integration and the concept of a normalizing constant, allows for primitive interjection of new parameter(s) in an existing distribution to form new model(s), called Omega-Type probability models. A probability distribution is proposed from a root model, Lindley distribution, and some properties, such as the series representation of the density and cumulative distribution functions, shape of the density, hazard and survival functions, moments and related measures, quantile function, order statistics, parameter estimation and interval estimate, were studied. Amidst the usual hazard and survival shapes, a constant or uniform trend was realized for the survival function, which projects the possibility of modeling systems that may not terminate over a given period of time. Three different methods of estimation, namely, the Cramer‒von Mises estimator, maximum product of the spacing estimator and maximum likelihood estimator, were used. The modified unimodal shape of the proposed distribution is added as a special feature in the improvements made among the Lindley family of distributions. Finally, two real-life datasets were fitted to the new distribution to demonstrate its economic importance.

Keywords: Omega-type model; Parametric modification; Lindley family; Survival trend; Data applications (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s40745-024-00539-y 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:12:y:2025:i:3:d:10.1007_s40745-024-00539-y

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

DOI: 10.1007/s40745-024-00539-y

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 ().