Modelling the distribution of grouped survival data via dependant neutral-to-the-right priors
Citation:
DONAGHY, FEARGHAL, Modelling the distribution of grouped survival data via dependant neutral-to-the-right priors, Trinity College Dublin.School of Computer Science & Statistics, 2020Download Item:
Abstract:
With each update of its browser, Firefox receives reports of the time of discovery of a
large number of bugs associated with that update. This process yields survival data
which is separated by update into groups and often exhibits much commonality. We
propose a model which, rather than treating each group separately, allows for borrowing
of information across the entire dataset. To this end, we use superposed completely
random measures to construct a vector of dependent neutral-to-the-right priors. The
model is completed by accounting for an unobserved number of right-censored data
points per group. An explicit characterisation of the posterior distribution of the defined vector of dependent neutral-to-the-right priors is derived and, in turn, used to devise an efficient marginal Markov chain Monte Carlo sampler for posterior inference. A
simulation study is carried out to assess the performance of the model. While motivated
by the Firefox data, our approach could potentially be useful across a wide range of
applications of survival analysis.
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https://tcdlocalportal.tcd.ie/pls/EnterApex/f?p=800:71:0::::P71_USERNAME:DONAGHYFDescription:
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Author: DONAGHY, FEARGHAL
Publisher:
Trinity College Dublin. School of Computer Science & Statistics. Discipline of StatisticsType of material:
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