Uncertain regression model based on Huber loss function

被引:4
|
作者
Xie, Wenxuan [1 ]
Wu, Jiali [1 ]
Sheng, Yuhong [1 ]
机构
[1] Xinjiang Univ, Coll Math & Syst Sci, Urumqi, Peoples R China
基金
中国国家自然科学基金;
关键词
Uncertain regression; Huber loss function; parameter estimation; uncertainty theory; uncertain variable;
D O I
10.3233/JIFS-223641
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In classic regression analysis, both the explanatory variables and response variables of the study are assumed to be exact data. However, in practical cases, we find some observations to be imprecise. Therefore, we regard the observed data as uncertain variables, and study the functional relationship between variables through uncertain regression analysis under the uncertainty theory. In this paper, we first propose Huber estimation based on the Huber loss function about uncertain regression model, which can effectively reduce the influence of outliers on the analysis results. Second, we put forward leave-one-out cross-validation method to select and adjust the unknown parameter in the Huber loss function. Then, a real numerical example illustrates the feasibility of Huber estimation. Finally, two simulated experimental examples are used to verify effectiveness of the estimation method for influence of outliers by comparing least squares and least absolute deviation.
引用
收藏
页码:1169 / 1178
页数:10
相关论文
共 50 条
  • [31] A new uncertain linear regression model based on equation deformation
    Shuai Wang
    Yufu Ning
    Hongmei Shi
    Soft Computing, 2021, 25 : 12817 - 12824
  • [32] A new uncertain linear regression model based on equation deformation
    Wang, Shuai
    Ning, Yufu
    Shi, Hongmei
    SOFT COMPUTING, 2021, 25 (20) : 12817 - 12824
  • [33] Uncertain hypothesis testing of multivariate uncertain regression model
    Zhang, Guidong
    Sheng, Yuhong
    Shi, Yuxin
    JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2022, 43 (06) : 7341 - 7350
  • [34] A new asymmetric ε-insensitive pinball loss function based support vector quantile regression model
    Anand, Pritam
    Rastogi, Reshma
    Chandra, Suresh
    APPLIED SOFT COMPUTING, 2020, 94
  • [35] Thermal Radiation Bias Correction for Infrared Images Using Huber Function-Based Loss
    Xie, Jun
    Song, Lingfei
    Huang, Hua
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2024, 62 : 1 - 15
  • [36] Pseudo-Huber loss function-based affine registration algorithm of point clouds
    Guo, Jiachen
    Ren, Lu
    Zhu, Xuan
    Zhuang, Jinlei
    Jiang, Bo
    Liu, Cheng
    Wang, Lin
    39TH YOUTH ACADEMIC ANNUAL CONFERENCE OF CHINESE ASSOCIATION OF AUTOMATION, YAC 2024, 2024, : 1041 - 1046
  • [37] Uncertain Linear Regression Analysis Model
    Wang, Zhigang
    Fu, Yiping
    Tian, Fanji
    INTERNATIONAL JOURNAL OF APPLIED MATHEMATICS & STATISTICS, 2014, 52 (06): : 12 - 21
  • [38] General and Uncertain Harmonic Source Model Based on Gaussian Process Regression
    Zhang Y.
    Liu B.
    Shao Z.
    Lin F.
    Lin C.
    Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering, 2022, 42 (03): : 992 - 1001
  • [39] MODEL FOR MACROECONOMIC ANALYSE BASED ON THE REGRESSION FUNCTION
    Anghelache, Constantin
    Pagliacci, Mario G. R.
    Prodan, Ligia
    ROMANIAN STATISTICAL REVIEW, 2013, (01) : 18 - 30
  • [40] Homography-Based Loss Function for Camera Pose Regression
    Boittiaux, Clementin
    Marxer, Ricard
    Dune, Claire
    Arnaubec, Aurelien
    Hugel, Vincent
    IEEE ROBOTICS AND AUTOMATION LETTERS, 2022, 7 (03) : 6242 - 6249