Learning Survival Distributions with the Asymmetric Laplace Distribution

TL;DR

This paper introduces a parametric survival analysis method using the Asymmetric Laplace Distribution, enabling efficient estimation of survival distributions and outperforming existing approaches in accuracy and calibration.

Contribution

It proposes a novel ALD-based parametric model for survival analysis that allows closed-form summaries and improves upon nonparametric methods.

Findings

Outperforms existing methods in accuracy and calibration

Provides closed-form survival summaries

Demonstrates effectiveness on synthetic and real data

Abstract

Probabilistic survival analysis models seek to estimate the distribution of the future occurrence (time) of an event given a set of covariates. In recent years, these models have preferred nonparametric specifications that avoid directly estimating survival distributions via discretization. Specifically, they estimate the probability of an individual event at fixed times or the time of an event at fixed probabilities (quantiles), using supervised learning. Borrowing ideas from the quantile regression literature, we propose a parametric survival analysis method based on the Asymmetric Laplace Distribution (ALD). This distribution allows for closed-form calculation of popular event summaries such as mean, median, mode, variation, and quantiles. The model is optimized by maximum likelihood to learn, at the individual level, the parameters (location, scale, and asymmetry) of the ALD distribution. Extensive results on synthetic and real-world data demonstrate that the proposed method outperforms parametric and nonparametric approaches in terms of accuracy, discrimination and calibration.