Mini-batch SGD optimizes a different objective than full partial likelihood in Cox models, but the resulting mb-MPLE is still consistent with optimal rates for neural nets and asymptotic normality for linear models.
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PL-Cox and GPL-Cox are new Gibbs samplers for Bayesian Cox models based on rank-ordered Plackett-Luce representations that avoid posterior corrections and handle ties and frailty.
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Mini-batch Estimation for Deep Cox Models: Statistical Foundations and Practical Guidance
Mini-batch SGD optimizes a different objective than full partial likelihood in Cox models, but the resulting mb-MPLE is still consistent with optimal rates for neural nets and asymptotic normality for linear models.
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Efficient Bayesian Inference in the Cox Model via Rank-Ordered Likelihood
PL-Cox and GPL-Cox are new Gibbs samplers for Bayesian Cox models based on rank-ordered Plackett-Luce representations that avoid posterior corrections and handle ties and frailty.