Synthetic.3: Synthetic Dataset #3: $p < n$ case
Description
Modeling survival model #3 as described in Dazard et al. (2015) with censoring.
Here, the regression function does not include any of the predictors.
This means that none of the covariates is informative (noisy), and 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, 3].
In this synthetic example, all covariates are continuous, i.i.d. from a multivariate uniform distribution on [0, 1].format
Each dataset consists of a numeric matrix containing $n=250$ observations (samples)
by rows and $p=3$ 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 #3 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.