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• W4383066743 abstract "Feature screening for ultrahigh-dimensional data, in general, proceeds with two essential steps. The first step is measuring and ranking the marginal dependence between response and covariates, and the second is determining the threshold. We develop a new screening procedure, called SIT-BY procedure, that possesses appealing statistical properties in both steps. By employing sliced independence estimates in the measuring and ranking stage, our proposed procedure requires no model assumptions, remains invariant to monotone transformation, and achieves almost linear computation complexity. Inspired by false discovery rate (FDR) control procedures, we offer a data-adaptive threshold benefit from the asymptotic normality of test statistics. Under moderate conditions, we demonstrate that our procedure can asymptotically control the FDR while maintaining the sure screening property. We investigate the finite sample performance of our proposed procedure via extensive simulations and an application to genome-wide dataset." @default.
• W4383066743 created "2023-07-05" @default.
• W4383066743 creator A5024232890 @default.
• W4383066743 creator A5068002238 @default.
• W4383066743 date "2024-01-01" @default.
• W4383066743 modified "2023-09-25" @default.
• W4383066743 title "Model-free, monotone invariant and computationally efficient feature screening with data-adaptive threshold" @default.
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• W4383066743 doi "https://doi.org/10.1016/j.jspi.2023.06.006" @default.
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