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Discrete Time Models

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

Abstract

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.

Keywords

Baseline Hazard Linear Predictor Discrete Time Model Proposal Distribution Continuous Time Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Fachmedien Wiesbaden 2015

Authors and Affiliations

  1. 1.HamburgGermany

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