Fuzzy regression analysis based on M-estimates

作者：

Highlights：

• The M-estimation approach is proposed for fuzzy regression analysis.

• The parameters estimation problem is reduced to an iterative reweighted algorithm.

• The algorithm decreases the effect of outliers by down-weighting them.

• The numerical results show the model is robust to the presence of outliers in data.

• The efficiency of the M-estimators is concluded by running sensitivity analysis.

摘要

•The M-estimation approach is proposed for fuzzy regression analysis.•The parameters estimation problem is reduced to an iterative reweighted algorithm.•The algorithm decreases the effect of outliers by down-weighting them.•The numerical results show the model is robust to the presence of outliers in data.•The efficiency of the M-estimators is concluded by running sensitivity analysis.

论文关键词：Fuzzy outlier,Goodness-of-fit,Huber function,Reweighted algorithm,Robustness

论文评审过程：Received 25 January 2021, Revised 6 September 2021, Accepted 6 September 2021, Available online 13 September 2021, Version of Record 20 September 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.115891