منابع مشابه
Sure 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...
متن کاملSure independence screening and compressed random sensing
Compressed sensing is a very powerful and popular tool for sparse recovery of high dimensional signals. Random sensing matrices are often employed in compressed sensing. In this paper we introduce a new method named aggressive betting using sure independence screening for sparse noiseless signal recovery. The proposal exploits the randomness structure of random sensing matrices to greatly boost...
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 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 کاملMarginal Empirical Likelihood and Sure Independence Feature Screening.
We study a marginal empirical likelihood approach in scenarios when the number of variables grows exponentially with the sample size. The marginal empirical likelihood ratios as functions of the parameters of interest are systematically examined, and we find that the marginal empirical likelihood ratio evaluated at zero can be used to differentiate whether an explanatory variable is contributin...
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ژورنال
عنوان ژورنال: Journal of the American Statistical Association
سال: 2016
ISSN: 0162-1459,1537-274X
DOI: 10.1080/01621459.2015.1092974