Sure independence screening for ultrahigh dimensional feature space
نویسندگان
چکیده
منابع مشابه
Rejoinder: Sure independence screening for ultrahigh dimensional feature space
We are very grateful to all contributors for their stimulating comments and questions on the role of variable screening and selection on high-dimensional statistical modeling. This paper would not have been in the current form without the benefits of private communications with Professors Peter Bickel, Peter Bühlmann, Eitan Greenshtein, Qiwei Yao, Cun-Hui Zhang and Wenyang Zhang at various stag...
متن کاملSure independence screening for ultrahigh dimensional feature space
High dimensionality is a growing feature in many areas of contemporary statistics. Variable selection is fundamental to high-dimensional statistical modeling. For problems of large or huge scale pn, computational cost and estimation accuracy are always two top concerns. In a seminal paper, Candes and Tao (2007) propose a minimum l1 estimator, the Dantzig selector, and show that it mimics the id...
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High dimensionality is a growing feature in many areas of contemporary statistics. Variable selection is fundamental to high-dimensional statistical modeling. For problems of large or huge scale pn, computational cost and estimation accuracy are always two top concerns. In a seminal paper, Candes and Tao (2007) propose a minimum l1 estimator, the Dantzig selector, and show that it mimics the id...
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Statistical inference can be computationally prohibitive in ultrahigh-dimensional linear models. Correlation-based variable screening, in which one leverages marginal correlations for removal of irrelevant variables from the model prior to statistical inference, can be used to overcome this challenge. Prior works on correlation-based variable screening either impose strong statistical priors on...
متن کاملDiscussion of "Sure Independence Screening for Ultra-High Dimensional Feature Space.
June 30, 2008 Abstract Variable selection plays an important role in high dimensional statistical modeling which nowadays appears in many areas and is key to various scientific discoveries. For problems of large scale or dimensionality p, estimation accuracy and computational cost are two top concerns. In a recent paper, Candes and Tao (2007) propose the Dantzig selector using L1 regularization...
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ژورنال
عنوان ژورنال: Journal of the Royal Statistical Society: Series B (Statistical Methodology)
سال: 2008
ISSN: 1369-7412,1467-9868
DOI: 10.1111/j.1467-9868.2008.00674.x