Mortality Prediction using Survival Energy Models with Functional Data Analysis
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866909099356061696 |
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| author | Mitsuta, Daiki Shimizu, Yasutaka |
| author_facet | Mitsuta, Daiki Shimizu, Yasutaka |
| contents | The Survival Energy Model (SEM), as originally introduced by Shimizu et al. (2020), is designed to characterize human bioenergetics by employing diffusion processes or inverse Gaussian processes. While parametric models have been employed to articulate the SEM, they exhibit inherent sensitivity in their parameters and hyperparameters, which in turn introduces issues of instability in estimation and prediction. In this paper, we demonstrate that the utilization of functional data analysis techniques for nonparametric estimation and prediction of critical functions within the SEM leads to a substantial enhancement in prediction performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_06138 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Mortality Prediction using Survival Energy Models with Functional Data Analysis Mitsuta, Daiki Shimizu, Yasutaka Applications 62M20, 62P05, 91D20 The Survival Energy Model (SEM), as originally introduced by Shimizu et al. (2020), is designed to characterize human bioenergetics by employing diffusion processes or inverse Gaussian processes. While parametric models have been employed to articulate the SEM, they exhibit inherent sensitivity in their parameters and hyperparameters, which in turn introduces issues of instability in estimation and prediction. In this paper, we demonstrate that the utilization of functional data analysis techniques for nonparametric estimation and prediction of critical functions within the SEM leads to a substantial enhancement in prediction performance. |
| title | Mortality Prediction using Survival Energy Models with Functional Data Analysis |
| topic | Applications 62M20, 62P05, 91D20 |
| url | https://arxiv.org/abs/2402.06138 |