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Most work in neural networks focuses on estimating the conditional mean of acontinuous response variable given a set of covariates.In this article, weconsider estimating the conditional distribution function using neural networksfor both censored and uncensored data. The algorithm is built upon the datastructure particularly constructed for the Cox regression with time-dependentcovariates. Without imposing any model assumption, we consider a loss functionthat is based on the full likelihood where the conditional hazard function isthe only unknown nonparametric parameter, for which unconstraint optimizationmethods can be applied. Through simulation studies, we show the proposed methodpossesses desirable performance, whereas the partial likelihood method and thetraditional neural networks with $L_2$ loss yield biased estimates when modelassumptions are violated. We further illustrate the proposed method withseveral real-world data sets. The implementation of the proposed methods ismade available at https://github.com/bingqing0729/NNCDE.

Conditional Distribution Function Estimation Using Neural Networks for Censored and Uncensored Data