Dataset from simulated regression survival model #4 as described in Dazard et al. (2015). Here, the regression function uses 1/10 of informative predictors in a p > n situation with p = 1000 and n = 100. The rest represents non-informative noisy covariates, which are not part of the design matrix. Survival time was generated from an exponential model with rate parameter λ (and mean 1/λ) according to a Cox-PH model with hazard exp(eta), where eta(.) is the regression function. Censoring indicator were generated from a uniform distribution on [0, 2]. In this synthetic example, all covariates are continuous, i.i.d. from a multivariate standard normal distribution.
Each dataset consists of a
matrix containing n=100 observations (samples)
by rows and p=1000 variables by columns, not including the censoring indicator and (censored) time-to-event variables.
It comes as a compressed Rda data file.
This work made use of the High Performance Computing Resource in the Core Facility for Advanced Research Computing at Case Western Reserve University. This project was partially funded by the National Institutes of Health NIH - National Cancer Institute (R01-CA160593) to J-E. Dazard and J.S. Rao.
"Jean-Eudes Dazard, Ph.D." [email protected]
"Michael Choe, M.D." [email protected]
"Michael LeBlanc, Ph.D." [email protected]
"Alberto Santana, MBA." [email protected]
Maintainer: "Jean-Eudes Dazard, Ph.D." [email protected]
See simulated survival model #4 in Dazard et al., 2015.
Dazard J-E. and Rao J.S. (2017). "Variable Selection Strategies for High-Dimensional Survival Bump Hunting using Recursive Peeling Methods." (in prep).
Diaz-Pachon D.A., Dazard J-E. and Rao J.S. (2017). "Unsupervised Bump Hunting Using Principal Components." In: Ahmed SE, editor. Big and Complex Data Analysis: Methodologies and Applications. Contributions to Statistics, vol. Edited Refereed Volume. Springer International Publishing, Cham Switzerland, p. 325-345.
Yi C. and Huang J. (2016). "Semismooth Newton Coordinate Descent Algorithm for Elastic-Net Penalized Huber Loss Regression and Quantile Regression." J. Comp Graph. Statistics, DOI: 10.1080/10618600.2016.1256816.
Dazard J-E., Choe M., LeBlanc M. and Rao J.S. (2016). "Cross-validation and Peeling Strategies for Survival Bump Hunting using Recursive Peeling Methods." Statistical Analysis and Data Mining, 9(1):12-42.
Dazard J-E., Choe M., LeBlanc M. and Rao J.S. (2015). "R package PRIMsrc: Bump Hunting by Patient Rule Induction Method for Survival, Regression and Classification." In JSM Proceedings, Statistical Programmers and Analysts Section. Seattle, WA, USA. American Statistical Association IMS - JSM, p. 650-664.
Dazard J-E., Choe M., LeBlanc M. and Rao J.S. (2014). "Cross-Validation of Survival Bump Hunting by Recursive Peeling Methods." In JSM Proceedings, Survival Methods for Risk Estimation/Prediction Section. Boston, MA, USA. American Statistical Association IMS - JSM, p. 3366-3380.
Dazard J-E. and J.S. Rao (2010). "Local Sparse Bump Hunting." J. Comp Graph. Statistics, 19(4):900-92.
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