Volume under the ROC surface for high-dimensional independent screening with ordinal competing risk outcomes

Author:

Qu Yang,Cheng YuORCID

Funder

National Science Foundation

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,General Medicine

Reference24 articles.

1. Budczies J, Kosztyla D (2021) cancerdata: development and validation of diagnostic tests from high-dimensional molecular data: datasets. R package version 1.30.0

2. Chen X, Li C, Zhang T, Gao Z (2022) On correlation rank screening for ultra-high dimensional competing risks data. J Appl Stat 49(7):1848–1864. https://doi.org/10.1080/02664763.2021.1884209

3. Fan J, Lv J (2008) Sure independence screening for ultrahigh dimensional feature space. J R Stat Soc Ser B (Stat Methodol) 70(5):849–911

4. Fan J, Feng Y, Wu Y (2010) High-dimensional variable selection for Cox’s proportional hazards model, Collections, vol 6. Institute of Mathematical Statistics, Beachwood, pp 70–86

5. Fine JP, Gray RJ (1999) A proportional hazards model for the subdistribution of a competing risk. J Am Stat Assoc 94(446):496–509

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