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

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Abstract

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.

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Authors and Affiliations

  1. College of Applied Sciences, Beijing University of Technology, Beijing, 100020, China

    Yu Zhong, Zhongzhan Zhang & Shoumei Li

Authors
  1. Yu Zhong
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  2. Zhongzhan Zhang
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  3. Shoumei Li
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Corresponding author

Correspondence to Shoumei Li.

Additional information

This research was supported by the National Nature Science Foundation of China under Grant Nos. 11571024 and 11771032, and the Humanities and Social Science Foundation of Ministry of Education of China under Grant No. 20YJCZH245.

This paper was recommended for publication by Editor XU Jin.

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Zhong, Y., Zhang, Z. & Li, S. A Constrained Interval-Valued Linear Regression Model: A New Heteroscedasticity Estimation Method. J Syst Sci Complex 33, 2048–2066 (2020). https://doi.org/10.1007/s11424-020-9075-2

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  • DOI: https://doi.org/10.1007/s11424-020-9075-2

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