Bayesian analysis of random effects panel interval-valued data models

Dengke Xu; Linlin Shen; Yuanyang Tangzhu; Shiqi Ke; Dengke Xu; Linlin Shen; Yuanyang Tangzhu; Shiqi Ke

doi:10.3934/era.2025141

Electronic Research Archive

2025, Volume 33, Issue 5: 3210-3224. doi: 10.3934/era.2025141

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Research article

Bayesian analysis of random effects panel interval-valued data models

School of Economics, Hangzhou Dianzi University, Hangzhou 310018, China

Received: 11 March 2025 Revised: 07 May 2025 Accepted: 16 May 2025 Published: 23 May 2025

In the era of big data, interval-valued data is quite common in real life and can be used to describe the uncertainty of variables. In this paper, we introduced random effects panel interval-valued data models based on the center and range method and constructed a Bayesian method for the models, including estimation and prediction. Some simulation studies indicate that the proposed Bayesian method performs well. Finally, our proposed panel interval-valued data Bayesian models were applied in forecasting of the Air Quality Index, and the experimental evaluation of actual data sets shows the advantages and the performance of our proposed models.
- panel data,
- interval-valued data,
- Bayesian estimation,
- center and range method
Citation: Dengke Xu, Linlin Shen, Yuanyang Tangzhu, Shiqi Ke. Bayesian analysis of random effects panel interval-valued data models[J]. Electronic Research Archive, 2025, 33(5): 3210-3224. doi: 10.3934/era.2025141

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Abstract

In the era of big data, interval-valued data is quite common in real life and can be used to describe the uncertainty of variables. In this paper, we introduced random effects panel interval-valued data models based on the center and range method and constructed a Bayesian method for the models, including estimation and prediction. Some simulation studies indicate that the proposed Bayesian method performs well. Finally, our proposed panel interval-valued data Bayesian models were applied in forecasting of the Air Quality Index, and the experimental evaluation of actual data sets shows the advantages and the performance of our proposed models.

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