Synthetic Dataset #4: p > n case


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 \frac{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 numeric 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.


Maintainer: "Jean-Eudes Dazard, Ph.D."

Acknowledgments: 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.


See simulated survival model #4 in Dazard et al., 2015.


  • Dazard J-E., Choe M., LeBlanc M. and Rao J.S. (2015). "Cross-validation and Peeling Strategies for Survival Bump Hunting using Recursive Peeling Methods." Statistical Analysis and Data Mining (in press).

  • 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., 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, (in press).

  • Dazard J-E. and J.S. Rao (2010). "Local Sparse Bump Hunting." J. Comp Graph. Statistics, 19(4):900-92.

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