Show simple item record

dc.contributor.authorNyambega, Henry Ondicho
dc.contributor.authorOrwa, George O
dc.contributor.authorMung'atu, Joseph K
dc.contributor.authorOtieno, Romanus Odhiambo
dc.date.accessioned2018-11-23T14:27:01Z
dc.date.accessioned2020-02-06T13:15:11Z
dc.date.available2018-11-23T14:27:01Z
dc.date.available2020-02-06T13:15:11Z
dc.date.issued2017
dc.identifier.citationNyambega, H. O., Orwa, G. O., Mung'atu, J. K., & Odhiambo, R. O. Bayesian Estimation of Survivor Function for Censored Data Using Lognormal Mixture Distributions.en_US
dc.identifier.urihttps://pdfs.semanticscholar.org/454e/4da5debf0bbffb640ce3eb054345526580a8.pdf
dc.identifier.urihttp://repository.must.ac.ke/handle/123456789/939
dc.description.abstractWe use Bayesian methods to fit a lognormal mixture model with two components to right censored survival data to estimate the survivor function. This is done using a simulation-based Bayesian framework employing a prior distribution of the Dirichlet process. The study provides an MCMC computational algorithm to obtaining the posterior distribution of a Dirichlet process mixture model (DPMM). In particular, Gibbs sampling through use of the WinBUGS package is used to generate random samples from the complex posterior distribution through direct successive simulations from the component conditional distributions. With these samples, a Dirichlet process mixture model with a lognormal kernel (DPLNMM) in the presence of censoring is implemented.en_US
dc.language.isoenen_US
dc.publisherIOSR Journal of Mathematicsen_US
dc.subjectBayesian, Lognormal, Survivor Function, Finite Mixture models, Win BUGSen_US
dc.titleBayesian Estimation of Survivor Function for Censored Data Using Lognormal Mixture Distributionsen_US
dc.typeArticleen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record