Model-free conditional screening via conditional distance correlation

Lü, Jun; Lin, Lu

doi:10.1007/s00362-017-0931-7

Cited by 21 publications

(8 citation statements)

References 24 publications

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“…The model-based methods usually enjoy high computational efficiency but have to bear the risk of model misspecification, which could lead to invalid screening results. To avoid such a risk, statisticians developed the model-free methods, some typical works include but are not limited to Li et al (2012), Cui et al (2015), Lu and Lin (2017), Pan et al (2019) and the reference therein. Additionally, to reduce the negative effect caused by the complicated correlation among predictors, researchers also put forward some conditional screening methods, see Barut et al (2015), Hu and Lin (2017), Lin and Sun (2016) and Lu and Lin (2017).…”

Section: Introductionmentioning

confidence: 99%

Feature screening for multi-response linear models by empirical likelihood

Azhari¹,

Li²

2022

Preprint

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This paper proposes a new feature screening method for the multi-response ultrahigh dimensional linear model by empirical likelihood. Through a multivariate moment condition, the empirical likelihood induced ranking statistics can exploit the joint effect among responses, and thus result in a much better performance than the methods considering responses individually. More importantly, by the use of empirical likelihood, the new method adapts to the heterogeneity in the conditional variance of random error. The sure screening property of the newly proposed method is proved with the model size controlled within a reasonable scale. Additionally, the new screening method is also extended to a conditional version so that it can recover the hidden predictors which are easily missed by the unconditional method. The corresponding theoretical properties are also provided. Finally, both numerical studies and real data analysis are provided to illustrate the effectiveness of the proposed methods.

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Section: Introductionmentioning

confidence: 99%

Feature screening for multi-response linear models by empirical likelihood

Azhari¹,

Li²

2022

Preprint

Self Cite

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“…Since then, conditional screening method has drawn much interest and various conditional screening approaches have been proposed under different scenarios. For example, Hu and Lin (2017), Liu and Wang (2018), and Lu and Lin (2020) developed different conditional screening procedures for ultrahigh-dimensional complete data, Liu and Chen (2018) considered the conditional quantile independence screening approach for ultrahigh-dimensional heterogeneous data, and Hong et al (2018) developed a conditional screening method for censored survival data under the proportional hazards model. Simulation studies demonstrated that these conditional screening approaches can provide a powerful means to identify hidden active variables for ultrahigh-dimensional data.…”

Section: Introductionmentioning

confidence: 99%

Model‐free conditional screening for ultrahigh‐dimensional survival data via conditional distance correlation

et al. 2022

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How to select the active variables that have significant impact on the event of interest is a very important and meaningful problem in the statistical analysis of ultrahigh‐dimensional data. In many applications, researchers often know that a certain set of covariates are active variables from some previous investigations and experiences. With the knowledge of the important prior knowledge of active variables, we propose a model‐free conditional screening procedure for ultrahigh dimensional survival data based on conditional distance correlation. The proposed procedure can effectively detect the hidden active variables that are jointly important but are weakly correlated with the response. Moreover, it performs well when covariates are strongly correlated with each other. We establish the sure screening property and the ranking consistency of the proposed method and conduct extensive simulation studies, which suggests that the proposed procedure works well for practical situations. Then, we illustrate the new approach through a real dataset from the diffuse large‐B‐cell lymphoma study S1.

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“…This method was extended to generalized linear models (Fan & Song, 2010), nonparametric additive models (Fan, Feng, & Song, 2011), and varying coefficient models (Fan, Ma, & Dai, 2014). Many model‐free screening methods were also provided in the literature; see, for example, Zhu, Li, Li, and Zhu (2011), Li, Zhong, and Zhu (2012), Huang and Zhu (2016), and Lu and Lin (2017). These model‐free methods can be used for aforementioned high‐dimensional computer experiments, and their performance is in need of evaluation.…”

Section: Introductionmentioning

confidence: 99%

“…and Zhu (2016), and Lu and Lin (2017). These model-free methods can be used for aforementioned high-dimensional computer experiments, and their performance is in need of evaluation.…”

mentioning

confidence: 99%

Linear screening for high‐dimensional computer experiments

Chen²,

Xiong

2021

Stat

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In this paper, we propose a linear variable screening method for computer experiments when the number of input variables is larger than the number of runs. This method uses a linear model to model the nonlinear data and screens important variables by existing screening methods for linear models. When the underlying simulator is nearly sparse, we prove that the linear screening method is asymptotically valid under mild conditions. To improve the screening accuracy for some extreme cases, we also provide a two-stage procedure that uses different basis functions in the linear model. The proposed methods are very simple and easy to implement. Numerical results indicate that our methods outperform existing model-free screening methods.

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Model-free conditional screening via conditional distance correlation

Cited by 21 publications

References 24 publications

Feature screening for multi-response linear models by empirical likelihood

Feature screening for multi-response linear models by empirical likelihood

Model‐free conditional screening for ultrahigh‐dimensional survival data via conditional distance correlation

Linear screening for high‐dimensional computer experiments

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