Statistical inference for Cox proportional hazards models with a diverging number of covariates
Statistical inference for Cox proportional hazards models with a diverging number of covariates
For statistical inference on regression models with a diverging number of covariates, the existing literature typically makes sparsity assumptions on the inverse of the Fisher information matrix. Such assumptions, however, are often violated under Cox proportion hazards models, leading to biased estimates with under-coverage confidence intervals. We propose a modified …