Sparse Robust Weighted Expectile Screening for Ultra-High-Dimensional Data
This paper investigates robust feature screening for ultra-high dimensional data in the presence of outliers and heterogeneity. Considering the susceptibility of likelihood methods to outliers, we propose a Sparse Robust Weighted Expectile Regression (SRoWER) method that combines the <inline-form...
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MDPI AG
2025-04-01
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| author | Xianjun Wu Pingping Han Mingqiu Wang |
| author_facet | Xianjun Wu Pingping Han Mingqiu Wang |
| author_sort | Xianjun Wu |
| collection | DOAJ |
| description | This paper investigates robust feature screening for ultra-high dimensional data in the presence of outliers and heterogeneity. Considering the susceptibility of likelihood methods to outliers, we propose a Sparse Robust Weighted Expectile Regression (SRoWER) method that combines the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>L</mi><mn>2</mn></msub><mi>E</mi></mrow></semantics></math></inline-formula> criterion with expectile regression. By utilizing the IHT algorithm, our method effectively incorporates correlations of covariates and enables joint feature screening. The proposed approach demonstrates robustness against heavy-tailed errors and outliers in data. Simulation studies and a real data analysis are provided to demonstrate the superior performance of the SRoWER method when dealing with outlier-contaminated explanatory variables and/or heavy-tailed error distributions. |
| format | Article |
| id | doaj-art-c9a7d3ae2a354469ae3e5db5a1db7ad8 |
| institution | OA Journals |
| issn | 2075-1680 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Axioms |
| spelling | doaj-art-c9a7d3ae2a354469ae3e5db5a1db7ad82025-08-20T02:33:39ZengMDPI AGAxioms2075-16802025-04-0114534010.3390/axioms14050340Sparse Robust Weighted Expectile Screening for Ultra-High-Dimensional DataXianjun Wu0Pingping Han1Mingqiu Wang2School of Statistics and Data Science, Qufu Normal University, Qufu 273100, ChinaSchool of Statistics and Data Science, Qufu Normal University, Qufu 273100, ChinaSchool of Statistics and Data Science, Qufu Normal University, Qufu 273100, ChinaThis paper investigates robust feature screening for ultra-high dimensional data in the presence of outliers and heterogeneity. Considering the susceptibility of likelihood methods to outliers, we propose a Sparse Robust Weighted Expectile Regression (SRoWER) method that combines the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>L</mi><mn>2</mn></msub><mi>E</mi></mrow></semantics></math></inline-formula> criterion with expectile regression. By utilizing the IHT algorithm, our method effectively incorporates correlations of covariates and enables joint feature screening. The proposed approach demonstrates robustness against heavy-tailed errors and outliers in data. Simulation studies and a real data analysis are provided to demonstrate the superior performance of the SRoWER method when dealing with outlier-contaminated explanatory variables and/or heavy-tailed error distributions.https://www.mdpi.com/2075-1680/14/5/340asymmetric least squaresfeature screeningheteroscedasticityrobust regressionultra-high dimensional data |
| spellingShingle | Xianjun Wu Pingping Han Mingqiu Wang Sparse Robust Weighted Expectile Screening for Ultra-High-Dimensional Data Axioms asymmetric least squares feature screening heteroscedasticity robust regression ultra-high dimensional data |
| title | Sparse Robust Weighted Expectile Screening for Ultra-High-Dimensional Data |
| title_full | Sparse Robust Weighted Expectile Screening for Ultra-High-Dimensional Data |
| title_fullStr | Sparse Robust Weighted Expectile Screening for Ultra-High-Dimensional Data |
| title_full_unstemmed | Sparse Robust Weighted Expectile Screening for Ultra-High-Dimensional Data |
| title_short | Sparse Robust Weighted Expectile Screening for Ultra-High-Dimensional Data |
| title_sort | sparse robust weighted expectile screening for ultra high dimensional data |
| topic | asymmetric least squares feature screening heteroscedasticity robust regression ultra-high dimensional data |
| url | https://www.mdpi.com/2075-1680/14/5/340 |
| work_keys_str_mv | AT xianjunwu sparserobustweightedexpectilescreeningforultrahighdimensionaldata AT pingpinghan sparserobustweightedexpectilescreeningforultrahighdimensionaldata AT mingqiuwang sparserobustweightedexpectilescreeningforultrahighdimensionaldata |