Papers › An Accurate Computational Approach for Partial Likelihood Using Poisson-Binomial Distributions
An Accurate Computational Approach for Partial Likelihood Using Poisson-Binomial Distributions
Youngjin Cho, Yili Hong, Pang Du
The archive published only this paper's code-link row. Authors, date and abstract are from arXiv's metadata (CC0), read from the Kaggle arXiv metadata snapshot of 2026-09-12 where its title matched the archive's; the title is the archive's.
In a Cox model, the partial likelihood, as the product of a series of conditional probabilities, is used to estimate the regression coefficients. In practice, those conditional probabilities are approximated by risk score ratios based on a continuous time model, and thus result in parameter estimates from only an approximate partial likelihood. Through a revisit to the original partial likelihood idea, an accurate partial likelihood computing method for the Cox model is proposed, which calculates the exact conditional probability using the Poisson-binomial distribution. New estimating and inference procedures are developed, and theoretical results are established for the proposed computational procedure. Although ties are common in real studies, current theories for the Cox model mostly do not consider cases for tied data. In contrast, the new approach includes the theory for grouped data, which allows ties, and also includes the theory for continuous data without ties, providing a unified framework for computing partial likelihood for data with or without ties. Numerical results show that the proposed method outperforms current methods in reducing bias and mean squared error, while achieving improved confidence interval coverage rates, especially when there are many ties or when the variability in risk scores is large. Comparisons between methods in real applications have been made.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections