Please use this identifier to cite or link to this item: https://doi.org/10.1111/j.1467-9574.2011.00481.x
DC FieldValue
dc.titleAnalysis of failure time using threshold regression with semi-parametric varying coefficients
dc.contributor.authorLi, J.
dc.contributor.authorLee, M.-L.T.
dc.date.accessioned2014-10-28T05:10:04Z
dc.date.available2014-10-28T05:10:04Z
dc.date.issued2011-05
dc.identifier.citationLi, J., Lee, M.-L.T. (2011-05). Analysis of failure time using threshold regression with semi-parametric varying coefficients. Statistica Neerlandica 65 (2) : 164-182. ScholarBank@NUS Repository. https://doi.org/10.1111/j.1467-9574.2011.00481.x
dc.identifier.issn00390402
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/105002
dc.description.abstractMany new statistical models may enjoy better interpretability and numerical stability than traditional models in survival data analysis. Specifically, the threshold regression (TR) technique based on the inverse Gaussian distribution is a useful alternative to the Cox proportional hazards model to analyse lifetime data. In this article we consider a semi-parametric modelling approach for TR and contribute implementational and theoretical details for model fitting and statistical inferences. Extensive simulations are carried out to examine the finite sample performance of the parametric and non-parametric estimates. A real example is analysed to illustrate our methods, along with a careful diagnosis of model assumptions. © 2011 The Authors. Statistica Neerlandica © 2011 VVS.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1111/j.1467-9574.2011.00481.x
dc.sourceScopus
dc.subjectBootstrap
dc.subjectInverse Gaussian distribution
dc.subjectThreshold regression
dc.subjectVarying coefficients model
dc.subjectWiener process
dc.typeArticle
dc.contributor.departmentSTATISTICS & APPLIED PROBABILITY
dc.description.doi10.1111/j.1467-9574.2011.00481.x
dc.description.sourcetitleStatistica Neerlandica
dc.description.volume65
dc.description.issue2
dc.description.page164-182
dc.identifier.isiut000289298400002
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