A kernel- and optimal transport- based test of independence between covariates and right-censored lifetimes
| Autor: | Dino Sejdinovic, David Rindt, David Steinsaltz |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Statistics and Probability Computer science Machine Learning (stat.ML) Mathematics - Statistics Theory Statistics Theory (math.ST) 030204 cardiovascular system & hematology 01 natural sciences 010104 statistics & probability 03 medical and health sciences 0302 clinical medicine Statistics - Machine Learning Resampling Covariate Statistics FOS: Mathematics 0101 mathematics Statistic Nonparametric statistics General Medicine Kernel method Kernel (statistics) Censoring (clinical trials) 62N03 Survival analysis and censored data Statistics Probability and Uncertainty Type I and type II errors |
| Popis: | We propose a nonparametric test of independence, termed optHSIC, between a covariate and a right-censored lifetime. Because the presence of censoring creates a challenge in applying the standard permutation-based testing approaches, we use optimal transport to transform the censored dataset into an uncensored one, while preserving the relevant dependencies. We then apply a permutation test using the kernel-based dependence measure as a statistic to the transformed dataset. The type 1 error is proven to be correct in the case where censoring is independent of the covariate. Experiments indicate that optHSIC has power against a much wider class of alternatives than Cox proportional hazards regression and that it has the correct type 1 control even in the challenging cases where censoring strongly depends on the covariate. |
| Databáze: | OpenAIRE |
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