Conditional Distribution Function Estimation Using Neural Networks for Censored and Uncensored Data.
Conditional Distribution Function Estimation Using Neural Networks for Censored and Uncensored Data.
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发表时间：
2023
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通讯作者：
Nan B
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作者：
Hu B;Nan B
神经网络中的大多数工作都集中在估计给定一组协变量的连续响应变量的条件均值。在这篇文章中，我们考虑使用神经网络估计条件分布函数的删失和未删失数据。该算法是建立在数据结构上，特别是构造的考克斯回归与时间相关的协变量。在不强加任何模型假设的情况下，我们考虑基于全似然的损失函数，其中条件风险函数是唯一未知的非参数参数，可以应用无约束优化方法。通过仿真研究，我们表明，所提出的方法具有良好的性能，而部分似然方法和传统的神经网络的损失产生有偏估计时，模型的假设被违反。我们进一步说明了所提出的方法与几个现实世界的数据集。在https://github.com/bingqing0729/NNCDE上提供了所提出的方法的实施。
Most work in neural networks focuses on estimating the conditional mean of a continuous response variable given a set of covariates. In this article, we consider estimating the conditional distribution function using neural networks for both censored and uncensored data. The algorithm is built upon the data structure particularly constructed for the Cox regression with time-dependent covariates. Without imposing any model assumptions, we consider a loss function that is based on the full likelihood where the conditional hazard function is the only unknown nonparametric parameter, for which unconstrained optimization methods can be applied. Through simulation studies, we show that the proposed method possesses desirable performance, whereas the partial likelihood method and the traditional neural networks with loss yields biased estimates when model assumptions are violated. We further illustrate the proposed method with several real-world data sets. The implementation of the proposed methods is made available at https://github.com/bingqing0729/NNCDE.