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家庭内肺炎链球菌携带传播：二元纵向数据的潜在马尔可夫过程模型AI 解读

We present a Bayesian data augmentation model to estimate acquisition and clearance rates of carriage of Streptococcus pneumoniae (Pnc) bacteria. The panel observation data comprise 10 measurements of Pnc carriage (carrier/noncarrier of the bacteria) in all members of 97 families with young children over a period of 2 years. Using natural conditional independence assumptions, a transmission model is constructed for the unobserved dependent binary processes of the augmented…

生命历程分析中的序贯预测模型评估AI 解读

We extend the prequential approach introduced by A. P. Dawid to continuous time marked pint processes. A prequential forecasting system is expressed by means of the stochastic intensities of the point process. The exact independence and 1-exponentiality of the derived compensators are used to assess the predictive performance of the model and the inferential procedure. Some key words : Bayesian inference; Goodness of fit; Marked pint process; Markov chain Monte Carlo;…

随机序约束下生存概率的贝叶斯推断AI 解读

In the statistical analysis of survival data arising from two populations, it often happens that the analyst knows, a priori, that the life lengths in one population are stochastically shorter than those in the other. Nevertheless, survival probability estimates, if determined separately from the corresponding samples, may not be consistent with this prior assumption, because of inherent statistical variability in the observations. This problem has been considered in a…

随机序约束下生存概率的贝叶斯推断AI 解读

In the statistical analysis of survival data arising from two populations, it often happens that the analyst knows, a priori, that the life lengths in one population are stochastically shorter than those in the other. Nevertheless, survival probability estimates, if determined separately from the corresponding samples, may not be consistent with this prior assumption, because of inherent statistical variability in the observations. This problem has been considered in a…

评估Cox比例风险模型拟合优度的图形方法AI 解读

Suggested here is a simple graphical method for studying the goodness of fit in Cox's regression model for survival data. The method is easy to use, as it does not require the estimation of alternative models and only involves quantities similar to those already appearing in the partial likelihood expression that is needed in the parameter estimation. The rationale behind the graphs is very intuitive: They make a direct comparison between observed and expected failure…