Synthetic.5: Synthetic Dataset #5: $p > n$ case
Description
Modeling survival model #5 as described in Dazard et al. (2015) with censoring.
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 $\lambda$ (and mean $\frac{1}{\lambda}$)
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.format
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.source
See simulated survival model #2 in Dazard et al., 2015.References
- 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."
(Submitted).
- 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.