期刊论文详细信息
Statistical Analysis and Data Mining | |
Regular, median and Huber cross‐validation: A computational comparison | |
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
【 预 览 】
Files | Size | Format | View |
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RO201904042629144ZK.pdf | 599KB | download |