Deep Neural Networks for Semiparametric Frailty Models via H-likelihood
- URL: http://arxiv.org/abs/2307.06581v1
- Date: Thu, 13 Jul 2023 06:46:51 GMT
- Title: Deep Neural Networks for Semiparametric Frailty Models via H-likelihood
- Authors: Hangbin Lee, IL DO HA, Youngjo Lee
- Abstract summary: We propose a new deep neural network based frailty (DNN-FM) for prediction of time-to-event data.
Joint estimators of the new h-likelihood model provide maximum likelihood for fixed parameters and best unbiased predictors for random frailties.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: For prediction of clustered time-to-event data, we propose a new deep neural
network based gamma frailty model (DNN-FM). An advantage of the proposed model
is that the joint maximization of the new h-likelihood provides maximum
likelihood estimators for fixed parameters and best unbiased predictors for
random frailties. Thus, the proposed DNN-FM is trained by using a negative
profiled h-likelihood as a loss function, constructed by profiling out the
non-parametric baseline hazard. Experimental studies show that the proposed
method enhances the prediction performance of the existing methods. A real data
analysis shows that the inclusion of subject-specific frailties helps to
improve prediction of the DNN based Cox model (DNN-Cox).
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