Artikel
Agu-Eghwerido distribution, regression model and applications
Modelling lifetime data with simple mathematical representations and an ease in obtain ing the parameter estimate of survival models are crucial quests pursued by survival re searchers. In this paper, we derived and introduced a one-parameter distribution called the Agu-Eghwerido (AGUE) distribution with its simple mathematical representation. The re gression model of the AGUE distribution was also presented. Several basic properties of the new distribution, such as reliability measures, mean residual function, median, moment gen erating function, skewness, kurtosis, coefficient of variation, and index of dispersion, were derived. The estimation of the proposed distribution parameter was based on the maximum likelihood estimation method. The real-life applications of the distribution were illustrated using two real lifetime negatively and positively skewed data sets. The new distribution pro vides a better fit than the Pranav, exponential, and Lindley distributions for the data sets. The simulation results showed that the increase in parameter values decreases the mean squared error value. Similarly, the mean estimate tends towards the true parameter value as the sample sizes increas.
- Sprache
-
Englisch
- Erschienen in
-
Journal: Statistics in Transition New Series ; ISSN: 2450-0291 ; Volume: 22 ; Year: 2021 ; Issue: 4 ; Pages: 59-76 ; New York: Exeley
- Thema
-
AGUE distribution
AGUE regression model
moment generating function,means residual function
hazard rate function
survival rate function
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Agu, Friday Ikechukwu
Eghwerido, Joseph Thomas
- Ereignis
-
Veröffentlichung
- (wer)
-
Exeley
- (wo)
-
New York
- (wann)
-
2021
- DOI
-
doi:10.21307/stattrans-2021-038
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:44 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Artikel
Beteiligte
- Agu, Friday Ikechukwu
- Eghwerido, Joseph Thomas
- Exeley
Entstanden
- 2021
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