Discrete Time Models

  • Matthias Kaeding
Part of the BestMasters book series (BEST)


As noted in section 2.3, by the introduction of failure indicators \({{y}_{ij}},\,i=1,\ldots ,n,j=1,\ldots ,{{t}_{i}}\) a Bernoulli likelihood is obtained and estimation can proceed as for binary regression – allowing that time can be treated like an arbitrary covariate whose effect can be smoothed. This is not the case for continuous time models.


Baseline Hazard Linear Predictor Discrete Time Model Proposal Distribution Continuous Time Model 
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Copyright information

© Springer Fachmedien Wiesbaden 2015

Authors and Affiliations

  1. 1.HamburgGermany

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