A Constrained Interval-Valued Linear Regression Model:A New Heteroscedasticity Estimation Method  

作　　者：ZHONG Yu ZHANG Zhongzhan LI Shoumei 

机构地区：[1]Beijing University of Technology,College of Applied Sciences,Beijing 100020,China

出　　处：《Journal of Systems Science & Complexity》2020年第6期2048-2066,共19页系统科学与复杂性学报（英文版）

基　　金：the National Nature Science Foundation of China under Grant Nos.11571024and 11771032;the Humanities and Social Science Foundation of Ministry of Education of China under Grant No.20YJCZH245。

摘　　要：Linear regression models for interval-valued data have been widely studied.Most literatures are to split an interval into two real numbers,i.e.,the left-and right-endpoints or the center and radius of this interval,and fit two separate real-valued or two dimension linear regression models.This paper is focused on the bias-corrected and heteroscedasticity-adjusted modeling by imposing order constraint to the endpoints of the response interval and weighted linear least squares with estimated covariance matrix,based on a generalized linear model for interval-valued data.A three step estimation method is proposed.Theoretical conclusions and numerical evaluations show that the proposed estimator has higher efficiency than previous estimators.

关 键 词：Conditional maximum likelihood estimation interval-valued data order constraint truncated normal distribution weighted least squares estimation 

分 类 号：O212.1[理学—概率论与数理统计]