AI RESEARCH
Mini-batch Estimation for Deep Cox Models: Statistical Foundations and Practical Guidance
arXiv CS.LG
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ArXi:2408.02839v3 Announce Type: replace-cross The stochastic gradient descent (SGD) algorithm has been widely used to optimize deep Cox neural network (Cox-NN) by updating model parameters using mini-batches of data. We show that SGD aims to optimize the average of mini-batch partial-likelihood, which is different from the standard partial-likelihood. This distinction requires developing new statistical properties for the global optimizer, namely, the mini-batch maximum partial-likelihood estimator (mb.