使用神经网络对有删减和无删减数据进行条件分布函数估计。

IF 4.3 3区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Journal of Machine Learning Research Pub Date : 2023-01-01
Bingqing Hu, Bin Nan
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引用次数: 0

摘要

神经网络方面的大多数研究工作都侧重于在给定一组协变量的情况下估计连续响应变量的条件均值。在本文中,我们将考虑使用神经网络估计有删减和无删减数据的条件分布函数。该算法建立在数据结构的基础上,特别是为具有时间相关协变量的 Cox 回归所构建的数据结构。在不强加任何模型假设的情况下,我们考虑了基于全似然的损失函数,其中条件危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。通过模拟研究,我们发现所提出的方法具有理想的性能,而部分似然法和带有 L2 损失的传统神经网络在违反模型假设时会产生有偏差的估计值。我们还用几个真实世界的数据集进一步说明了所提出的方法。建议方法的实现可在 https://github.com/bingqing0729/NNCDE 上获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Conditional Distribution Function Estimation Using Neural Networks for Censored and Uncensored Data.

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 L2 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.

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来源期刊
Journal of Machine Learning Research
Journal of Machine Learning Research 工程技术-计算机:人工智能
CiteScore
18.80
自引率
0.00%
发文量
2
审稿时长
3 months
期刊介绍: The Journal of Machine Learning Research (JMLR) provides an international forum for the electronic and paper publication of high-quality scholarly articles in all areas of machine learning. All published papers are freely available online. JMLR has a commitment to rigorous yet rapid reviewing. JMLR seeks previously unpublished papers on machine learning that contain: new principled algorithms with sound empirical validation, and with justification of theoretical, psychological, or biological nature; experimental and/or theoretical studies yielding new insight into the design and behavior of learning in intelligent systems; accounts of applications of existing techniques that shed light on the strengths and weaknesses of the methods; formalization of new learning tasks (e.g., in the context of new applications) and of methods for assessing performance on those tasks; development of new analytical frameworks that advance theoretical studies of practical learning methods; computational models of data from natural learning systems at the behavioral or neural level; or extremely well-written surveys of existing work.
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