منابع مشابه
Sure 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
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 کاملA discussion on “ Sure independence screening for ultrahigh di - mensional
mensional feature space” by J. Fan and L. Lv, Christian P. Robert, CEREMADE, Université Paris Dauphine and CREST, INSEE While I appreciate the “tour de force” involved in the paper, including the proof that P(M? ⊂ Mγ) converges, I cannot but get an overall feeling of slight disbelief about the statistical consequences of the results contained in the paper: in short, I basically question the per...
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 Screening for Gaussian Graphical Models
We propose graphical sure screening, or GRASS, a very simple and computationally-efficient screening procedure for recovering the structure of a Gaussian graphical model in the high-dimensional setting. The GRASS estimate of the conditional dependence graph is obtained by thresholding the elements of the sample covariance matrix. The proposed approach possesses the sure screening property: with...
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ژورنال
عنوان ژورنال: Statistica Sinica
سال: 2021
ISSN: 1017-0405
DOI: 10.5705/ss.202018.0462