Negative Binomial Kumaraswamy-G Cure Rate Regression Model

Amanda D’Andrea, Ricardo Rocha, Vera Tomazella and Francisco Louzada
Additional contact information
Amanda D’Andrea: Department of Statistics, Federal University of São Carlos, São Carlos, SP 13565-905, Brazil
Ricardo Rocha: Department of Statistics, Institute of Mathematics and Statistics, Federal University of Bahia, Salvador, BA 40170-115, Brazil
Vera Tomazella: Department of Statistics, Federal University of São Carlos, São Carlos, SP 13565-905, Brazil
Francisco Louzada: Institute of Mathematical Science and Computing, University of São Paulo, São Carlos, SP 13565-905, Brazil

JRFM, 2018, vol. 11, issue 1, 1-14

Abstract: In survival analysis, the presence of elements not susceptible to the event of interest is very common. These elements lead to what is called a fraction cure, cure rate, or even long-term survivors. In this paper, we propose a unified approach using the negative binomial distribution for modeling cure rates under the Kumaraswamy family of distributions. The estimation is made by maximum likelihood. We checked the maximum likelihood asymptotic properties through some simulation setups. Furthermore, we propose an estimation strategy based on the Negative Binomial Kumaraswamy-G generalized linear model. Finally, we illustrate the distributions proposed using a real data set related to health risk.

Keywords: long-term survivors; Kumaraswamy family; survival analysis; negative binomial distribution; generalized linear model (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/1911-8074/11/1/6/pdf (application/pdf)
https://www.mdpi.com/1911-8074/11/1/6/ (text/html)

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:gam:jjrfmx:v:11:y:2018:i:1:p:6-:d:127861

Access Statistics for this article

JRFM is currently edited by Ms. Chelthy Cheng

More articles in JRFM from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().