De-noising boosting methods for variable selection and estimation subject to error-prone variables
Boosting is one of the most powerful statistical learning methods that combines multiple weak learners into a strong learner. The main idea of boosting is to sequentially apply the algorithm to enhance its performance. Recently, boosting methods have been implemented to handle variable selection. Ho...
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| Published in: | Statistics and computing Vol. 33; no. 2 |
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| Format: | Journal Article |
| Language: | English |
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01.04.2023
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| ISSN: | 0960-3174, 1573-1375 |
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| Abstract | Boosting is one of the most powerful statistical learning methods that combines multiple weak learners into a strong learner. The main idea of boosting is to sequentially apply the algorithm to enhance its performance. Recently, boosting methods have been implemented to handle variable selection. However, little work has been available to deal with complex data such as measurement error in covariates. In this paper, we adopt the boosting method to do variable selection, especially in the presence of measurement error. We develop two different approximated correction approaches to deal with different types of responses, and meanwhile, eliminate measurement error effects. In addition, the proposed algorithms are easy to implement and are able to derive precise estimators. Throughout numerical studies under various settings, the proposed method outperforms other competitive approaches. |
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| AbstractList | Boosting is one of the most powerful statistical learning methods that combines multiple weak learners into a strong learner. The main idea of boosting is to sequentially apply the algorithm to enhance its performance. Recently, boosting methods have been implemented to handle variable selection. However, little work has been available to deal with complex data such as measurement error in covariates. In this paper, we adopt the boosting method to do variable selection, especially in the presence of measurement error. We develop two different approximated correction approaches to deal with different types of responses, and meanwhile, eliminate measurement error effects. In addition, the proposed algorithms are easy to implement and are able to derive precise estimators. Throughout numerical studies under various settings, the proposed method outperforms other competitive approaches. |
| ArticleNumber | 38 |
| Author | Chen, Li-Pang |
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| Cites_doi | 10.1002/sim.8130 10.1080/01621459.1996.10476682 10.1080/01621459.1997.10474001 10.1111/j.1541-0420.2006.00578.x 10.1214/20-EJS1762 10.1214/07-STS242A 10.1016/j.spl.2020.108931 10.1080/10618600.2018.1425626 10.1002/(SICI)1097-0258(19970130)16:2<169::AID-SIM478>3.0.CO;2-M 10.1111/biom.13331 10.1080/00949655.2020.1800705 10.1080/10618600.2016.1247005 10.1198/016214506000000735 10.1111/biom.13112 10.1214/aos/1176349155 10.3150/09-BEJ205 10.1214/009053604000000067 10.1198/jasa.2011.tm10098 10.1198/016214501753382273 10.1111/j.1467-9868.2005.00503.x 10.1201/9781420010138 10.1007/s10463-020-00755-2 10.1111/j.2517-6161.1996.tb02080.x |
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| Keywords | SIMEX Regression calibration Error correction Statistical learning Mismeasurement Generalized linear models |
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| References | Chen (CR8) 2020; 90 Wolfson (CR25) 2011; 106 Sørensen, Hellton, Frigessi, Thoresen (CR21) 2018; 27 Hastie (CR16) 2007; 22 Carroll, Fan, Gijbels, Wand (CR6) 1997; 92 Ma, Li (CR19) 2010; 16 CR12 Chen, Yi (CR11) 2021; 77 Chen, Yi (CR10) 2020; 14 Carroll, Küchenhoff, Lombard, Stefanski (CR5) 1996; 91 Tutz, Binder (CR23) 2006; 62 Fan, Li (CR14) 2001; 96 Hall, Li (CR15) 1993; 21 Küchenhoff, Carroll (CR18) 1997; 16 Efron, Hastie, Johnstone, Tibshirani (CR13) 2004; 32 Hastie, Tibshirani, Friedman (CR17) 2009 Wang (CR24) 2000; 10 Candes, Tao (CR4) 2007; 35 Nghiem, Potgieter (CR20) 2019; 75 Zou (CR26) 2006; 101 Carroll, Ruppert, Stefanski, Crainiceanu (CR7) 2006 Brown, Miller, Wolfson (CR1) 2017; 26 Bühlmann, Hothorn (CR3) 2007; 22 Chen (CR9) 2021; 168 Tibshirani (CR22) 1996; 58 Zou, Hastie (CR27) 2005; 67 Brown, Weaver, Wolfson (CR2) 2019; 38 G Tutz (10209_CR23) 2006; 62 Ø Sørensen (10209_CR21) 2018; 27 RJ Carroll (10209_CR7) 2006 L-P Chen (10209_CR9) 2021; 168 L-P Chen (10209_CR11) 2021; 77 E Candes (10209_CR4) 2007; 35 10209_CR12 P Bühlmann (10209_CR3) 2007; 22 B Efron (10209_CR13) 2004; 32 L-P Chen (10209_CR8) 2020; 90 L Nghiem (10209_CR20) 2019; 75 Y Ma (10209_CR19) 2010; 16 B Brown (10209_CR1) 2017; 26 R Tibshirani (10209_CR22) 1996; 58 H Zou (10209_CR26) 2006; 101 RJ Carroll (10209_CR5) 1996; 91 T Hastie (10209_CR16) 2007; 22 RJ Carroll (10209_CR6) 1997; 92 J Wolfson (10209_CR25) 2011; 106 CY Wang (10209_CR24) 2000; 10 L-P Chen (10209_CR10) 2020; 14 J Fan (10209_CR14) 2001; 96 H Zou (10209_CR27) 2005; 67 B Brown (10209_CR2) 2019; 38 T Hastie (10209_CR17) 2009 H Küchenhoff (10209_CR18) 1997; 16 P Hall (10209_CR15) 1993; 21 |
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| Title | De-noising boosting methods for variable selection and estimation subject to error-prone variables |
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