Functional Bayesian networks for discovering causality from multivariate functional data
© 2023 The Authors. Biometrics published by Wiley Periodicals LLC on behalf of International Biometric Society..
Multivariate functional data arise in a wide range of applications. One fundamental task is to understand the causal relationships among these functional objects of interest. In this paper, we develop a novel Bayesian network (BN) model for multivariate functional data where conditional independencies and causal structure are encoded by a directed acyclic graph. Specifically, we allow the functional objects to deviate from Gaussian processes, which is the key to unique causal structure identification even when the functions are measured with noises. A fully Bayesian framework is designed to infer the functional BN model with natural uncertainty quantification through posterior summaries. Simulation studies and real data examples demonstrate the practical utility of the proposed model.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:79 |
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| Enthalten in: |
Biometrics - 79(2023), 4 vom: 01. Dez., Seite 3279-3293 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Zhou, Fangting [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Completed 21.12.2023 Date Revised 07.02.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1111/biom.13922 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a © 2023 The Authors. Biometrics published by Wiley Periodicals LLC on behalf of International Biometric Society. | ||
| 520 | |a Multivariate functional data arise in a wide range of applications. One fundamental task is to understand the causal relationships among these functional objects of interest. In this paper, we develop a novel Bayesian network (BN) model for multivariate functional data where conditional independencies and causal structure are encoded by a directed acyclic graph. Specifically, we allow the functional objects to deviate from Gaussian processes, which is the key to unique causal structure identification even when the functions are measured with noises. A fully Bayesian framework is designed to infer the functional BN model with natural uncertainty quantification through posterior summaries. Simulation studies and real data examples demonstrate the practical utility of the proposed model | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, U.S. Gov't, Non-P.H.S. | |
| 650 | 4 | |a Research Support, N.I.H., Extramural | |
| 650 | 4 | |a causal discovery | |
| 650 | 4 | |a directed acyclic graphs | |
| 650 | 4 | |a multivariate longitudinal/functional data | |
| 650 | 4 | |a non-Gaussianity | |
| 650 | 4 | |a structure learning | |
| 700 | 1 | |a He, Kejun |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Kunbo |e verfasserin |4 aut | |
| 700 | 1 | |a Xu, Yanxun |e verfasserin |4 aut | |
| 700 | 1 | |a Ni, Yang |e verfasserin |4 aut | |
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