2023/05/01 by Hiroki Yanagisawa, Yanagisawa, Hiroki · 2 citations
Mathematics · Computer Science · #Statistical Methods and Inference #Bayesian Modeling and Causal Inference #Statistical Methods and Bayesian Inference
paper · pdf · doi:10.48550/arxiv.2305.00621
Survival analysis is the problem of estimating probability distributions for future event times, which can be seen as a problem in uncertainty quantification. Although there are fundamental theories on strictly proper scoring rules for uncertainty quantification, little is known about those for survival analysis. In this paper, we investigate extensions of four major strictly proper scoring rules for survival analysis and we prove that these extensions are proper under certain conditions, which arise from the discretization of the estimation of probability distributions. We also compare the estimation performances of these extended scoring rules by using real datasets, and the extensions of the logarithmic score and the Brier score performed the best.