Model-Free Feature Screening for Ultrahigh-Dimensional Data
With the recent explosion of scientific data of unprecedented size and complexity, feature ranking and screening are playing an increasingly important role in many scientific studies. In this article, we propose a novel feature screening procedure under a unified model framework, which covers a wide...
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| Vydáno v: | Journal of the American Statistical Association Ročník 106; číslo 496; s. 1464 - 1475 |
|---|---|
| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Alexandria, VA
Taylor & Francis
01.12.2011
American Statistical Association Taylor & Francis Ltd |
| Témata: | |
| ISSN: | 0162-1459, 1537-274X |
| On-line přístup: | Získat plný text |
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| Abstract | With the recent explosion of scientific data of unprecedented size and complexity, feature ranking and screening are playing an increasingly important role in many scientific studies. In this article, we propose a novel feature screening procedure under a unified model framework, which covers a wide variety of commonly used parametric and semiparametric models. The new method does not require imposing a specific model structure on regression functions, and thus is particularly appealing to ultrahigh-dimensional regressions, where there are a huge number of candidate predictors but little information about the actual model forms. We demonstrate that, with the number of predictors growing at an exponential rate of the sample size, the proposed procedure possesses consistency in ranking, which is both useful in its own right and can lead to consistency in selection. The new procedure is computationally efficient and simple, and exhibits a competent empirical performance in our intensive simulations and real data analysis. |
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| AbstractList | With the recent explosion of scientific data of unprecedented size and complexity, feature ranking and screening are playing an increasingly important role in many scientific studies. In this article, we propose a novel feature screening procedure under a unified model framework, which covers a wide variety of commonly used parametric and semiparametric models. The new method does not require imposing a specific model structure on regression functions, and thus is particularly appealing to ultrahigh-dimensional regressions, where there are a huge number of candidate predictors but little information about the actual model forms. We demonstrate that, with the number of predictors growing at an exponential rate of the sample size, the proposed procedure possesses consistency in ranking, which is both useful in its own right and can lead to consistency in selection. The new procedure is computationally efficient and simple, and exhibits a competent empirical performance in our intensive simulations and real data analysis. With the recent explosion of scientific data of unprecedented size and complexity, feature ranking and screening are playing an increasingly important role in many scientific studies. In this article, we propose a novel feature screening procedure under a unified model framework, which covers a wide variety of commonly used parametric and semiparametric models. The new method does not require imposing a specific model structure on regression functions, and thus is particularly appealing to ultrahigh-dimensional regressions, where there are a huge number of candidate predictors but little information about the actual model forms. We demonstrate that, with the number of predictors growing at an exponential rate of the sample size, the proposed procedure possesses consistency in ranking, which is both useful in its own right and can lead to consistency in selection. The new procedure is computationally efficient and simple, and exhibits a competent empirical performance in our intensive simulations and real data analysis. [PUBLICATION ABSTRACT] With the recent explosion of scientific data of unprecedented size and complexity, feature ranking and screening are playing an increasingly important role in many scientific studies. In this article, we propose a novel feature screening procedure under a unified model framework, which covers a wide variety of commonly used parametric and semiparametric models. The new method does not require imposing a specific model structure on regression functions, and thus is particularly appealing to ultrahigh-dimensional regressions, where there are a huge number of candidate predictors but little information about the actual model forms. We domonstrate that, with the number of predictors growing at an exponential rate of the sample size, the proposed procedure possesses consistency in ranking, which is both useful in its own right and can lead to consistency in selection. The new procedure is computationally efficient and simple, and exhibits a competent empirical performance in our intensive simulations and real data analysis. With the recent explosion of scientific data of unprecedented size and complexity, feature ranking and screening are playing an increasingly important role in many scientific studies. In this article, we propose a novel feature screening procedure under a unified model framework, which covers a wide variety of commonly used parametric and semiparametric models. The new method does not require imposing a specific model structure on regression functions, and thus is particularly appealing to ultrahigh-dimensional regressions, where there are a huge number of candidate predictors but little information about the actual model forms. We demonstrate that, with the number of predictors growing at an exponential rate of the sample size, the proposed procedure possesses consistency in ranking, which is both useful in its own right and can lead to consistency in selection. The new procedure is computationally efficient and simple, and exhibits a competent empirical performance in our intensive simulations and real data analysis.With the recent explosion of scientific data of unprecedented size and complexity, feature ranking and screening are playing an increasingly important role in many scientific studies. In this article, we propose a novel feature screening procedure under a unified model framework, which covers a wide variety of commonly used parametric and semiparametric models. The new method does not require imposing a specific model structure on regression functions, and thus is particularly appealing to ultrahigh-dimensional regressions, where there are a huge number of candidate predictors but little information about the actual model forms. We demonstrate that, with the number of predictors growing at an exponential rate of the sample size, the proposed procedure possesses consistency in ranking, which is both useful in its own right and can lead to consistency in selection. The new procedure is computationally efficient and simple, and exhibits a competent empirical performance in our intensive simulations and real data analysis. |
| Author | Li, Runze Zhu, Li-Ping Li, Lexin Zhu, Li-Xing |
| Author_xml | – sequence: 1 givenname: Li-Ping surname: Zhu fullname: Zhu, Li-Ping – sequence: 2 givenname: Lexin surname: Li fullname: Li, Lexin – sequence: 3 givenname: Runze surname: Li fullname: Li, Runze – sequence: 4 givenname: Li-Xing surname: Zhu fullname: Zhu, Li-Xing |
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| ContentType | Journal Article |
| Copyright | 2011 American Statistical Association
2011 2011 The American Statistical Association 2015 INIST-CNRS Copyright American Statistical Association Dec 2011 |
| Copyright_xml | – notice: 2011 American Statistical Association 2011 – notice: 2011 The American Statistical Association – notice: 2015 INIST-CNRS – notice: Copyright American Statistical Association Dec 2011 |
| DBID | AAYXX CITATION IQODW NPM 8BJ FQK JBE K9. 7X8 5PM |
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| Keywords | Rank statistic Data analysis Sample size Explosions Parametric model Semiparametric model Parametric method Statistical method Statistical regression Selection problem Sampling theory Simulation Regression model Ultrahigh-dimensional regression Sample survey Feature ranking Variable selection |
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| References | p_27 p_17 p_28 p_2 p_18 p_1 p_19 p_3 p_14 p_25 p_26 Fan J. (p_12) 2009; 10 Tibshirani R. (p_24) 1996; 58 p_7 p_9 Fan J. (p_10) 2010; 20 p_20 p_11 p_22 Härdle W. (p_15) 1993; 21 Pettitt A. N. (p_21) 1982; 44 Cox D. R. (p_5) 1972; 34 15713732 - Bioinformatics. 2005 May 15;21(10):2403-9 19603084 - J R Stat Soc Series B Stat Methodol. 2008;70(5):849-911 18344565 - Biostatistics. 2008 Oct;9(4):658-67 12075054 - N Engl J Med. 2002 Jun 20;346(25):1937-47 21603590 - J Mach Learn Res. 2009;10:2013-2038 15256406 - Bioinformatics. 2004 Dec 12;20(18):3406-12 21572976 - Stat Sin. 2010 Jan;20(1):101-148 |
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| Title | Model-Free Feature Screening for Ultrahigh-Dimensional Data |
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