A reversible jump Markov chain Monte Carlo approach to hazard estimation for competing risks data

Bibliographic Details
Main Author:	Bradley, Jeremy
Published:	University of Oxford 2002
Subjects:	616 Survival curves
Online Access:	http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.289357

id	ndltd-bl.uk-oai-ethos.bl.uk-289357
record_format	oai_dc
spelling	ndltd-bl.uk-oai-ethos.bl.uk-2893572015-03-19T10:39:47ZA reversible jump Markov chain Monte Carlo approach to hazard estimation for competing risks dataBradley, Jeremy2002616Survival curvesUniversity of Oxfordhttp://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.289357Electronic Thesis or Dissertation
collection	NDLTD
sources	NDLTD
topic	616 Survival curves
spellingShingle	616 Survival curves Bradley, Jeremy A reversible jump Markov chain Monte Carlo approach to hazard estimation for competing risks data
author	Bradley, Jeremy
author_facet	Bradley, Jeremy
author_sort	Bradley, Jeremy
title	A reversible jump Markov chain Monte Carlo approach to hazard estimation for competing risks data
title_short	A reversible jump Markov chain Monte Carlo approach to hazard estimation for competing risks data
title_full	A reversible jump Markov chain Monte Carlo approach to hazard estimation for competing risks data
title_fullStr	A reversible jump Markov chain Monte Carlo approach to hazard estimation for competing risks data
title_full_unstemmed	A reversible jump Markov chain Monte Carlo approach to hazard estimation for competing risks data
title_sort	reversible jump markov chain monte carlo approach to hazard estimation for competing risks data
publisher	University of Oxford
publishDate	2002
url	http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.289357
work_keys_str_mv	AT bradleyjeremy areversiblejumpmarkovchainmontecarloapproachtohazardestimationforcompetingrisksdata AT bradleyjeremy reversiblejumpmarkovchainmontecarloapproachtohazardestimationforcompetingrisksdata
_version_	1716776128621838336

A reversible jump Markov chain Monte Carlo approach to hazard estimation for competing risks data

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