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Li、van Dyk与Autenreith对《组合回归》的讨论AI 解读

Si-Yang Li, David A van Dyk, Maximilian Autenrieth; Si-Yang Li, David van Dyk and Maximilian Autenreith's contribution to the Discussion of ‘Regression by

部分折叠 Gibbs 采样器AI 解读

Ever-increasing computational power, along with ever–more sophisticated statistical computing techniques, is making it possible to fit ever–more complex statistical models. Among the more computationally intensive methods, the Gibbs sampler is popular because of its simplicity and power to effectively generate samples from a high-dimensional probability distribution. Despite its simple implementation and description, however, the Gibbs sampler is criticized for its sometimes…

一般处理机制下的因果推断AI 解读

In this article we develop the theoretical properties of the propensity function, which is a generalization of the propensity score of Rosenbaum and Rubin. Methods based on the propensity score have long been used for causal inference in observational studies; they are easy to use and can effectively reduce the bias caused by nonrandom treatment assignment. Although treatment regimes need not be binary in practice, the propensity score methods are generally confined to…

含半连续变量的不完整数据多重插补AI 解读

We consider the application of multiple imputation to data containing not only partially missing categorical and continuous variables, but also partially missing 'semicontinuous' variables (variables that take on a single discrete value with positive probability but are otherwise continuously distributed). As an imputation model for data sets of this type, we introduce an extension of the standard general location model proposed by Olkin and Tate; our extension, the blocked…