{
  "id": "2307.06581",
  "version": "v1",
  "published": "2023-07-13T06:46:51.000Z",
  "updated": "2023-07-13T06:46:51.000Z",
  "title": "Deep Neural Networks for Semiparametric Frailty Models via H-likelihood",
  "authors": [
    "Hangbin Lee",
    "IL DO HA",
    "Youngjo Lee"
  ],
  "categories": [
    "stat.ML",
    "cs.LG",
    "stat.ME"
  ],
  "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).",
  "revisions": [
    {
      "version": "v1",
      "updated": "2023-07-13T06:46:51.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "deep neural network",
      "semiparametric frailty models",
      "h-likelihood",
      "gamma frailty model",
      "maximum likelihood estimators"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}