Covariate dimension reduction for survival data via the Gaussian process latent variable model
Covariate dimension reduction for survival data via the Gaussian process latent variable model
The analysis of high‐dimensional survival data is challenging, primarily owing to the problem of overfitting, which occurs when spurious relationships are inferred from data that subsequently fail to exist in test data. Here, we propose a novel method of extracting a low‐dimensional representation of covariates in survival data by combining …