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Spearman Rank Correlation Screening for Ultrahigh-Dimensional Censored Data

AAAI 2023 · Probabilistic and scientific modeling › Variational and generative sampling · approach: Likelihood-free and graphical model estimation · 16 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use censoring-adjusted Spearman screening instead of censored rank-correlation screeningScreening followed by SCAD in the linear model · Linear model, CR=65%CP (%): 11.00 -> 86.50Helped
Use censoring-adjusted Spearman screening instead of censored rank-correlation screeningLinear transformation model, normal errors · CR=65%, random censoringP_all: 0.00 -> 0.92Helped
Use censoring-adjusted Spearman screening instead of censored quantile screeningLinear transformation model, normal errors · CR=65%, random censoringP_all: 0.86 -> 0.92Helped
Use censoring-adjusted Spearman screening instead of censored quantile screeningScreening followed by SCAD in the linear model · Linear model, CR=45%CP (%): 63.50 -> 91.50Helped
Use censoring-adjusted Spearman screening instead of censored quantile screeningAdditive model · CR=45%P_all: 0.90 -> 0.97Helped
Use censoring-adjusted Spearman screening instead of censored quantile screeningSingle-index regression model · CR=65%P_all: 0.92 -> 0.95Helped
Replace SCAD with MCP (ablation)SRCS_cen screening in the linear model · CR=45%RMS: 0.49 -> 0.49No clear effect
Replace SCAD with LASSO (ablation)SRCS_cen screening in the linear model · CR=45%RMS: 0.49 -> 0.86Hurt
Apply screening methods designed for complete responses to censored responsesTraditional SRCS, CQS, and RCS screening · Linear model, CR=65%CP (%): SRCS: 22.50; CQS(0.5): 4.50; RCS: 14.00Hurt
Increase censoring from CR=45% to CR=65%RCS_cen screening · Linear transformation model, normal errors, random censoringP_all: 0.92 -> 0.00Hurt
Increase censoring from CR=45% to CR=65%SRCS_cen screening · Linear transformation model, normal errors, random censoringP_all: 1 -> 0.92Hurt

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