Sökning: onr:"swepub:oai:DiVA.org:hj-54761" >
A new biased estima...
-
Akram, Muhammad N.Department of Statistics, University of Sargodha, Sargodha, Pakistan
(författare)
A new biased estimator for the gamma regression model : Some applications in medical sciences
- Artikel/kapitelEngelska2023
Förlag, utgivningsår, omfång ...
-
2021-09-20
-
Taylor & Francis,2023
-
printrdacarrier
Nummerbeteckningar
-
LIBRIS-ID:oai:DiVA.org:hj-54761
-
https://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-54761URI
-
https://doi.org/10.1080/03610926.2021.1977958DOI
Kompletterande språkuppgifter
-
Språk:engelska
-
Sammanfattning på:engelska
Ingår i deldatabas
Klassifikation
-
Ämneskategori:ref swepub-contenttype
-
Ämneskategori:art swepub-publicationtype
Anmärkningar
-
The Gamma Regression Model (GRM) has a variety of applications in medical sciences and other disciplines. The results of the GRM may be misleading in the presence of multicollinearity. In this article, a new biased estimator called James-Stein estimator is proposed to reduce the impact of correlated regressors for the GRM. The mean squared error (MSE) properties of the proposed estimator are derived and compared with the existing estimators. We conducted a simulation study and employed the MSE and bias evaluation criterion to judge the proposed estimator’s performance. Finally, two medical dataset are considered to show the benefit of the proposed estimator over existing estimators.
Ämnesord och genrebeteckningar
Biuppslag (personer, institutioner, konferenser, titlar ...)
-
Amin, MuhammadDepartment of Statistics, University of Sargodha, Sargodha, Pakistan
(författare)
-
Qasim, MuhammadJönköping University,IHH, Nationalekonomi, Finansiering och Statistik(Swepub:hj)qasmuh
(författare)
-
Department of Statistics, University of Sargodha, Sargodha, PakistanIHH, Nationalekonomi, Finansiering och Statistik
(creator_code:org_t)
Sammanhörande titlar
-
Ingår i:Communications in Statistics - Theory and Methods: Taylor & Francis52:11, s. 3612-36320361-09261532-415X
Internetlänk
Hitta via bibliotek
Till lärosätets databas