# Testing and Confidence Intervals for High Dimensional Proportional Hazards Model

arXiv: https://arxiv.org/abs/1412.5158 · HTML: https://mlsanity.com/paper/1412.5158/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Dec 2014.

## Built on by (most cited first, 6 of 6)

- [Inference for High Dimensional Censored Quantile Regression](https://mlsanity.com/paper/2107.10959/) (2021)
- [Statistical Inference for Linear Functionals of Online SGD in High-dimensional Linear Regression](https://mlsanity.com/paper/2302.09727/) (2023)
- [An Overview of Healthcare Data Analytics With Applications to the COVID-19 Pandemic](https://mlsanity.com/paper/2111.14623/) (2021)
- [High-dimensional Inference and FDR Control for Simulated Markov Random Fields](https://mlsanity.com/paper/2202.05612/) (2022)
- [Communication-Efficient Distributed Estimation and Inference for Cox's Model](https://mlsanity.com/paper/2302.12111/) (2023)
- [Survival of the fittest Cox model: Pivotal variable selection for time-to-event data](https://mlsanity.com/paper/2510.19374/) (2025)

## Abstract (arXiv)

This paper proposes a decorrelation-based approach to test hypotheses and construct confidence intervals for the low dimensional component of high dimensional proportional hazards models. Motivated by the geometric projection principle, we propose new decorrelated score, Wald and partial likelihood ratio statistics. Without assuming model selection consistency, we prove the asymptotic normality of these test statistics, establish their semiparametric optimality. We also develop new procedures for constructing pointwise confidence intervals for the baseline hazard function and baseline survival function. Thorough numerical results are provided to back up our theory.