A Bayesian parametrized method for interval-valued regression models

作者：Xu, Min; Qin, Zhongfeng^*

来源：STATISTICS AND COMPUTING, 2023, 33(3): 67.

DOI：10.1007/s11222-023-10234-2

• Parametrized method
• Interval-valued data
• Bayesian method
• EM algorithm

Interval-valued data, as typical symbolic data, provide a feasible way to deal with massive data sets. Although a lot of literature has been focused on researching interval-valued regression models, few works are devoted to exploring Bayesian methods for interval-valued data. In this paper, we propose a novel Bayesian parametrized method for interval-valued data by transforming an interval into a reference point, and further establish a Bayesian linear regression model. The advantage of the Bayesian parametrized method is to make use of full information within intervals and meanwhile it can solve the potential problem of multicollinearity in the parametrized method. We assume the prior distribution is normal with zero mean, and employ the EM algorithm to obtain the empirical Bayes estimates. The results of experimental and real data sets show that the Bayesian parametrized method has a superior forecasting advantage when the sample size is small and the random error is large.

• 单位
  北京工业大学; 北京航空航天大学