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Constrained center and range joint model for interval-valued symbolic data regression

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  • Hao, Peng
  • Guo, Junpeng

Abstract

A constrained center and range joint model to fit linear regression to interval-valued symbolic data is introduced. This new method applies both the center and range of the interval to fit a linear regression model, and avoids the negative value of the range of the predicted dependent interval variable by adding nonnegative constraints. To improve prediction accuracy it adopts overlapping constraints. Using a little algebra, it is constructed as a special case of the least squares with inequality (LSI) problem and is solved with a Matlab routine. The assessment of the proposed prediction method is based on an estimation of the average root mean square error and accuracy rate. In the framework of a Monte Carlo experiment, different data set configurations take into account the rich or lack of error, as well as the slope with respect to the dependent and independent variables. A statistical t-test compares the performance of the new model with that of four previously reported methods. Based on experiment results, it is outlined that the new model has better fitness. An analysis of outliers is performed to determine the effects of outliers on our proposal. The proposed method is illustrated by analyses of data from two real-life case studies to compare its performance with those of the other methods.

Suggested Citation

  • Hao, Peng & Guo, Junpeng, 2017. "Constrained center and range joint model for interval-valued symbolic data regression," Computational Statistics & Data Analysis, Elsevier, vol. 116(C), pages 106-138.
    • Handle: RePEc:eee:csdana:v:116:y:2017:i:c:p:106-138
    DOI: 10.1016/j.csda.2017.06.005
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    Cited by:

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    2. Wagner J. F. Silva & Renata M. C. R. Souza & F. J. A. Cysneiros, 2022. "Bivariate elliptical regression for modeling interval-valued data," Computational Statistics, Springer, vol. 37(4), pages 2003-2028, September.
    3. Qing Zhao & Huiwen Wang & Shanshan Wang, 2023. "Robust regression for interval-valued data based on midpoints and log-ranges," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 17(3), pages 583-621, September.

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