<abstract xmlns="http://www.w3.org/1999/xhtml"><p> We introduce the Weibull distributions in presence of cure fraction, censored data and covariates. Two models are explored in this paper: mixture and non-mixture models. Inferences for the proposed models are obtained under the Bayesian approach, using standard MCMC (Markov Chain Monte Carlo) methods. An illustration of the proposed methodology is given considering a life- time data set.</p></abstract>

We introduce the Weibull distributions in presence of cure fraction, censored data and covariates. Two models are explored in this paper: mixture and non-mixture models. Inferences for the proposed models are obtained under the Bayesian approach, using standard MCMC (Markov Chain Monte Carlo) methods. An illustration of the proposed methodology is given considering a life- time data set.

Cure fraction models using mixture and non-mixture models

Department of Social Medicine—FMRP University of S˜ao Paulo Ribeir˜ao Preto, SP BRAZIL

Josmar Mazucheli Department of Statistics University of Maring´a Paran´a BRAZIL

Tatra Mountains Mathematical Publications

{"article-title":"Cure fraction models using mixture and non-mixture models"}

We introduce the Weibull distributions in presence of cure fraction, censored data and covariates. Two models are explored in this paper: mixture and...

Cure fraction models using mixture and non-mixture models

Online veröffentlicht: 13. Nov. 2012

Seitenbereich: 1 - 9

DOI: https://doi.org/10.2478/v10127-012-0001-4

Schlüsselwörter
cure fraction, mixture model, non-mixture model, Weibull distribution, lifetime data.

This content is open access.

Cure fraction models using mixture and non-mixture models

Online veröffentlicht: 13. Nov. 2012

Seitenbereich: 1 - 9

DOI: https://doi.org/10.2478/v10127-012-0001-4

Schlüsselwörtercure fraction, mixture model, non-mixture model, Weibull distribution, lifetime data.

This content is open access.

Schlüsselwörter
cure fraction, mixture model, non-mixture model, Weibull distribution, lifetime data.