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聚类数量的近似置信区间AI 解读

We consider clustering for the purpose of data reduction. Similar objects are grouped together in clusters so that one can then work with the few cluster descriptors instead of the many data points. The quality of any given clustering is measured by a loss function that takes into account both the parsimony of the clustering and the loss of information due to clustering. An optimal clustering can be obtained by minimizing the theoretical loss function. It is shown that a…

聚类数量的近似置信区间AI 解读

We consider clustering for the purpose of data reduction. Similar objects are grouped together in clusters so that one can then work with the few cluster descriptors instead of the many data points. The quality of any given clustering is measured by a loss function that takes into account both the parsimony of the clustering and the loss of information due to clustering. An optimal clustering can be obtained by minimizing the theoretical loss function. It is shown that a…

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Kaplan–Meier 生存曲线估计的置信带AI 解读

The weak convergence of the Kaplan-Meier estimate for censored survival data to a Gaussian process is used to construct asymptotic confidence bands for the survival curves. The method involves transforming to obtain Brownian motion and using straight line boundaries and hitting probabilities for Brownian motion.