论文标题
审查的分位数回归神经网络用于无分布生存分析
Censored Quantile Regression Neural Networks for Distribution-Free Survival Analysis
论文作者
论文摘要
本文考虑使用神经网络（NNS）对审查数据进行分位回归。通过允许使用灵活的函数近似器，通过允许直接预测目标变量，以及不确定性的无分布表征来增加生存分析工具包。首先，我们展示了如何将线性模型中流行的算法应用于NNS。但是，所得过程效率低下，需要在每个所需的分位数处对单个NN进行顺序优化。我们的主要贡献是一种新型算法，该算法同时通过单个NN优化了分位数输出的网格。为了对我们的算法提供理论上的见解，我们首先证明它可以解释为一种期望最大化的形式，其次，它具有理想的“自我校正”属性。在实验上，该算法产生的分位数比12个真实数据集中的10个现有方法更好地校准。
This paper considers doing quantile regression on censored data using neural networks (NNs). This adds to the survival analysis toolkit by allowing direct prediction of the target variable, along with a distribution-free characterisation of uncertainty, using a flexible function approximator. We begin by showing how an algorithm popular in linear models can be applied to NNs. However, the resulting procedure is inefficient, requiring sequential optimisation of an individual NN at each desired quantile. Our major contribution is a novel algorithm that simultaneously optimises a grid of quantiles output by a single NN. To offer theoretical insight into our algorithm, we show firstly that it can be interpreted as a form of expectation-maximisation, and secondly that it exhibits a desirable `self-correcting' property. Experimentally, the algorithm produces quantiles that are better calibrated than existing methods on 10 out of 12 real datasets.
