Conditional Sure Independence Screening

Barut, Emre; Fan, Jianqing; Verhasselt, Anneleen

doi:10.1080/01621459.2015.1092974

Cited by 110 publications

(154 citation statements)

References 26 publications

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“…As shown in Barut et al (2012), conditional information can help reducing the correlation among the variables. They proposed a conditional sure independence screening (CSIS) by the known active predictors which makes it possible to recover the hidden importance variables and reduce the number of false negatives.…”

Section: Introductionmentioning

confidence: 97%

“…They proposed a conditional sure independence screening (CSIS) by the known active predictors which makes it possible to recover the hidden importance variables and reduce the number of false negatives. But the CSIS in Barut et al (2012) has a strongly restrictive for distributional model assumptions and needs to estimate β C repeatedly when individually measuring the strength of the conditional contribution of the rest variables given X C .…”

Section: Introductionmentioning

confidence: 99%

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Conditional sure independence screening by conditional marginal empirical likelihood

Lin²

2015

Ann Inst Stat Math

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In many applications, researchers often know a certain set of predictors is related to the response from some previous investigations and experiences. Based on the conditional information, we propose a conditional screening feature procedure via ranking conditional marginal empirical likelihood ratios. Due to the use of centralized variable, the proposed screening approach works well when there exist either or both hidden important variables and unimportant variables that are highly marginal correlated with the response. Moreover, the new method is demonstrated effective in scenarios with less restrictive distributional assumptions by inheriting the advantage of empirical likelihood approach and is computationally simple because it only needs to evaluate the conditional marginal empirical likelihood ratio at one point, without parameter estimation and iterative algorithm. The theoretical results reveal that the proposed procedure has sure screening properties. The merits of the procedure are illustrated by extensive numerical examples.

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Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Conditional sure independence screening by conditional marginal empirical likelihood

Lin²

2015

Ann Inst Stat Math

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“…However, the marginal screening approaches, such as PSIS and FAST, were not designed to integrate prior knowledge into variable screening. Recent years have seen an advent of conditional sure independence screening methods that use a priori information; see [1], [12]. By including such important predictors as the conditioning variables, conditional screening ranks the marginal utility of each variable after adjusting for these conditioning variables.…”

Section: Conditional Screeningmentioning

confidence: 99%

“…The error ε ~ N(0, 1) is independent of X. The censoring time C was generated from a 3-component normal mixture distribution N(0, 4) − N(5, 1) + 0.5N (25,1). This example was adopted from [17].…”

Section: §3 Numerical Comparisonsmentioning

confidence: 99%

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Feature selection of ultrahigh-dimensional covariates with survival outcomes: a selective review

Hong

2017

Appl. Math. J. Chin. Univ.

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Many modern biomedical studies have yielded survival data with high-throughput predictors. The goals of scientific research often lie in identifying predictive biomarkers, understanding biological mechanisms and making accurate and precise predictions. Variable screening is a crucial first step in achieving these goals. This work conducts a selective review of feature screening procedures for survival data with ultrahigh dimensional covariates. We present the main methodologies, along with the key conditions that ensure sure screening properties. The practical utility of these methods is examined via extensive simulations. We conclude the review with some future opportunities in this field. MR Subject Classification 97K80Keywords survival analysis; ultrahigh dimensional predictors; variable screening; sure screening property §1 Introduction Modern biomedical studies have generated abundant survival data with high dimensional biomarkers for various scientific purposes. For instance, identifying genomic profiles that are associated with cancer patients' survival may help with understanding disease progression processes and designing more effective gene therapies. With the advent of new biotechnologies, the emergence of high-throughput data, such as gene expressions, SNPs, methylation and next-generation RNA sequencing, has pushed the dimensionality of data to a larger scale. In these cases, the dimensionality of covariates may grow exponentially with the sample size and such data has been commonly referred to as ultrahigh dimensional data ([5]).When the number of covariates (p) is less than the sample size (n), the parametric regression, such as Weibull models, and the semiparametric regression, such as the Cox proportional hazards model and the Accelerated Failure Time (AFT) model, have been routinely used for modeling censored outcome data in many practical settings. When p > n, penalized ([20], [4], [25], [29]) and the oracle properties and statistical error bounds of estimation have been established ([13], [15]). However, when p ≫ n, computational issues inherent in these methods makes them nonapplicable to ultrahigh-dimensional statistical learning problems because of serious challenges in "computational expediency, statistical accuracy, and algorithmic stability" ([6]). A recent work by [2] did establish the oracle properties of the regularized partial likelihood estimates under an ultrahigh dimensional setting. The results, however, required the optimizers to the penalized partial likelihood function to be unique and global, which is, in general, difficult to verify, especially when the dimension of covariates is exceedingly high. HHS Public AccessA seminal paper by [5] has demonstrated a simple but useful way to deal with ultrahigh dimensional regression. First, a variable screening procedure is used as a fast and crude tool for reducing the dimensionality to a moderate size (usually below the sample size). In the second step, a more sophisticated technique, such as penalized likelihood methods, c...

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Sure Independence Screening

Fan

2018

Wiley StatsRef: Statistics Reference Online

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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, ultrahigh‐dimensional data analysis poses fundamental challenges to scalable learning and inference with good statistical efficiency. Sure independence screening is a simple and effective method to this endeavor. This framework of two‐scale statistical learning, consisting of large‐scale screening followed by moderate‐scale variable selection introduced in Fan and Lv (2008), has been extensively investigated and extended to various model settings ranging from parametric to semiparametric and nonparametric for regression, classification, and survival analysis. This article provides an overview of the developments of sure independence screening over the past decade. These developments demonstrate the wide applicability of the sure independence screening‐based learning and inference for big data analysis with desired scalability and theoretical guarantees.

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Conditional Sure Independence Screening

Cited by 110 publications

References 26 publications

Conditional sure independence screening by conditional marginal empirical likelihood

Conditional sure independence screening by conditional marginal empirical likelihood

Feature selection of ultrahigh-dimensional covariates with survival outcomes: a selective review

Sure Independence Screening

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