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dc.date.accessioned 2021-11-29T15:59:59Z
dc.date.available 2021-11-29T15:59:59Z
dc.date.issued 2019-07
dc.identifier.uri http://sedici.unlp.edu.ar/handle/10915/128804
dc.description.abstract In Survival Analysis, the focus of interest is a time T* until the occurrence of some event. A set of explanatory variables (denoted by a vector Z) is considered to analyze if there is a relationship between any of them and T*. Accordingly, the "hazard function" is defined: λ(t, z): = limΔ↓0 (P [T ≤ t + Δ | T > t, Z = z] / Δ) Several models are defined based on this, as is the case of the additive model (among others). Bayesian techniques allow to incorporate previous knowledge or presumption information about the parameters into the model. This area grows extensively since the computationally techniques increase, giving rise to powerful Markov Chain Monte Carlo (MCMC) methods, which allow to generate random samples from the desired distributions. The purpose of this article is to offer a summary of the research developed in Bayesian techniques to approach the additive hazard models. en
dc.language en es
dc.subject Survival analysis es
dc.subject Bayesian inference es
dc.subject Additive hazards model es
dc.title Review of Bayesian Analysis in Additive Hazards Model en
dc.type Articulo es
sedici.identifier.other doi:10.9734/ajpas/2019/v4i230112 es
sedici.identifier.issn 2582-0230 es
sedici.creator.person Álvarez, Enrique Ernesto es
sedici.creator.person Riddick, Maximiliano Luis es
sedici.subject.materias Ciencias Exactas es
sedici.subject.materias Matemática es
sedici.description.fulltext true es
mods.originInfo.place Facultad de Ciencias Exactas es
mods.originInfo.place Facultad de Ingeniería es
sedici.subtype Articulo es
sedici.rights.license Creative Commons Attribution 4.0 International (CC BY 4.0)
sedici.rights.uri http://creativecommons.org/licenses/by/4.0/
sedici.description.peerReview peer-review es
sedici.relation.journalTitle Asian Journal of Probability and Statistics es
sedici.relation.journalVolumeAndIssue vol. 4, no. 2 es

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