Regular, median and Huber cross‐validation: A computational comparison

Statistical Analysis and Data Mining

ChiWai Yu¹  Bertrand Clarke² 

[1] Department of Mathematics The Hong Kong University of Science and Technology Clearwater Bay, Kowloon, Hong Kong;Department of Statistics University of Nebraska‐Lincoln Lincoln NE 68583 USA

关键词: cross‐;    validation;    model selection;    heavy‐;    tailed errors;    robustness;    skewness;    sparsity;    outliers;   

DOI  :  10.1002/sam.11254

学科分类：社会科学、人文和艺术（综合）

来源: John Wiley & Sons, Inc.

【 摘 要 】

Abstract: We present a new technique for comparing models using a median form of cross-validation and least median of squares estimation (MCV-LMS). Rather than minimizing the sums of squares of residual errors, we minimize the median of the squared residual errors. We compare this with a robustified form of cross-validation using the Huber loss function and robust coefficient estimators (HCV). Through extensive simulations we find that for linear models MCV-LMS outperforms HCV for data that is r.

【 授权许可】

Unknown