AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Novel methods to broaden the scope of landmark analysis by directly modeling the restricted residual mean survival time (RRMST) at each landmark date while accommodating dependent censoring are proposed.
This work proposes an Efficiency-Aware Surrogate-adjusted Estimator (EASE) that directly chooses the sampling law and surrogate to minimize the leading mean squared error (MSE), and demonstrates that EASE consistently outperforms existing estimators for various probabilistic values.
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