The Lomax regression model with residual analysis: an application to insurance data
Emrah Altun
Journal of Applied Statistics, 2021, vol. 48, issue 13-15, 2515-2524
Abstract:
In this paper, we introduce a new regression model, called Lomax regression model, as an alternative to the gamma regression model. The maximum-likelihood method is used to estimate the unknown parameters of the proposed model, and the finite sample performance of the maximum-likelihood estimation method is evaluated by means of the Monte-Carlo simulation study. The randomized quantile residuals are used to check the adequacy of the fitted model. The insurance data are analyzed to demonstrate the usefulness of the proposed regression model against the gamma regression model.
Date: 2021
References: Add references at CitEc
Citations:
Downloads: (external link)
http://hdl.handle.net/10.1080/02664763.2020.1834515 (text/html)
Access to full text is restricted to subscribers.
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:taf:japsta:v:48:y:2021:i:13-15:p:2515-2524
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/CJAS20
DOI: 10.1080/02664763.2020.1834515
Access Statistics for this article
Journal of Applied Statistics is currently edited by Robert Aykroyd
More articles in Journal of Applied Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().