تقرير
Deep Neural Networks for Semiparametric Frailty Models via H-likelihood
| العنوان: | Deep Neural Networks for Semiparametric Frailty Models via H-likelihood |
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| المؤلفون: | Lee, Hangbin, HA, IL DO, Lee, Youngjo |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science Statistics |
| مصطلحات موضوعية: | Statistics - Machine Learning, Computer Science - Machine Learning, Statistics - Methodology |
| الوصف: | 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). |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2307.06581 |
| رقم الأكسشن: | edsarx.2307.06581 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |