Sure independence screening in generalized linear models with NP-dimensionality
نویسندگان
چکیده
منابع مشابه
SURE INDEPENDENCE SCREENING IN GENERALIZED LINEAR MODELS WITH NP-DIMENSIONALITY∗ By
Princeton University and Colorado State University Ultrahigh dimensional variable selection plays an increasingly important role in contemporary scientific discoveries and statistical research. Among others, Fan and Lv (2008) propose an independent screening framework by ranking the marginal correlations. They showed that the correlation ranking procedure possesses a sure independence screening...
متن کاملSure Independence Screening with NP-dimensionality
Ultrahigh dimensional variable selection plays an increasingly important role in contemporary scientific discoveries and statistical research. A simple and effective method is the correlation screening. For generalized linear models, we propose a more general version of the independent learning with ranking the maximum marginal likelihood estimates or the maximum marginal likelihood itself. We ...
متن کاملSure Independence Screening in Generalized Linear Models with Np-dimensionality1 By
Ultrahigh-dimensional variable selection plays an increasingly important role in contemporary scientific discoveries and statistical research. Among others, Fan and Lv [J. R. Stat. Soc. Ser. B Stat. Methodol. 70 (2008) 849–911] propose an independent screening framework by ranking the marginal correlations. They showed that the correlation ranking procedure possesses a sure independence screeni...
متن کاملExSIS: Extended Sure Independence Screening for Ultrahigh-dimensional Linear Models
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 کاملSure Independence Screening
Big data is ubiquitous in various fields of sciences, engineering, medicine, social sciences, and humanities. It is often accompanied by a large number of variables and features. While adding much greater flexibility to modeling with enriched feature space, ultra-high dimensional data analysis poses fundamental challenges to scalable learning and inference with good statistical efficiency. Sure...
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ژورنال
عنوان ژورنال: The Annals of Statistics
سال: 2010
ISSN: 0090-5364
DOI: 10.1214/10-aos798