Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
Distribution-valued data graphical model estimation based on M-LDQ feature embedding. |
| Συγγραφείς: |
Wu, Qiying1,2 (AUTHOR), Wang, Huiwen1,3 (AUTHOR), Lu, Shan4 (AUTHOR) shan.lu@cufe.edu.cn |
| Πηγή: |
Journal of Applied Statistics. Jun2026, Vol. 53 Issue 8, p1538-1561. 24p. |
| Θεματικοί όροι: |
*Data analysis, *Statistical hypothesis testing, Nonparametric estimation, Data distribution |
| Εταιρία/Οντότητα: |
Shanghai Stock Exchange |
| Περίληψη: |
Understanding and modeling distribution-valued data, an important form of symbolic data, has garnered significant attention in statistics because of its effectiveness in handling large datasets. Conventional statistical inference methods are not directly applicable to distribution-valued data, which has prompted extensive research efforts aimed at addressing this challenge. However, graphical models, which are powerful tools in applied statistics, have not yet been fully developed for distribution-valued data. To fill this gap, this study proposes a novel nonparametric graphical model estimation method for distribution-valued data. The proposed method first removes the inherent constraints of distributions, effectively capturing both position information (as a scalar) and shape information (as a function). We subsequently propose an aggregation method, which is based on the conditional independence test, to integrate the position information and shape information for graphical model estimation. Several numerical simulations have validated that our method outperforms other potential competing methods. Furthermore, we apply our method to construct the network of stocks that constitute the SSE 50 Index using daily distribution-valued data of five-minute returns. The empirical results reveal sector-specific relationships as well as cross-sector influences, highlighting the evolving interconnections between stocks from different sectors over time. [ABSTRACT FROM AUTHOR] |
|
Copyright of Journal of Applied Statistics is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Βάση Δεδομένων: |
Business Source Index |