Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: 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 debiased lasso
approach, which solves a series of quadratic programming problems to
approximate the inverse information matrix without posing sparse matrix
assumptions. We establish asymptotic results for the estimated regression
coefficients when the dimension of covariates diverges with the sample size. As
demonstrated by extensive simulations, our proposed method provides consistent
estimates and confidence intervals with nominal coverage probabilities. The
utility of the method is further demonstrated by assessing the effects of
genetic markers on patients' overall survival with the Boston Lung Cancer
Survival Cohort, a large-scale epidemiology study investigating mechanisms
underlying the lung cancer.

31
The categories of this paper are: ao-sci
9
The categories of this paper are: nlin
1
The categories of this paper are: cs
19
The categories of this paper are: eess
36
The categories of this paper are: cmp-lg
30
The categories of this paper are: q-alg
27
The categories of this paper are: comp-gas
2
The categories of this paper are: math
0
The categories of this paper are: hep-ph
34
The categories of this paper are: chem-ph
26
The categories of this paper are: bayes-an
14
The categories of this paper are: math-ph
5
The categories of this paper are: gr-qc
33
The categories of this paper are: plasm-ph
11
The categories of this paper are: hep-lat
15
The categories of this paper are: nucl-ex