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人工超级智能的决策理论视角：预测与处理选择中的缺失数据问题AI 解读

Enormous attention and resources are being devoted to the quest for artificial general intelligence and, even more ambitiously, artificial superintelligence. We wonder about the implications for methodological research that aims to help decision makers cope with what econometricians call identification problems, inferential problems in empirical research that do not diminish as sample size grows. Of particular concern are missing data problems in prediction and treatment…

多阶段分层抽样与学生未参与下国际大型测评平均成绩的部分识别AI 解读

International large-scale assessment (ILSA) studies collect information across education systems with the objective of learning about the population-wide distribution of student achievement in the assessment. In this article, we study one of the most fundamental threats that these studies face when justifying the conclusions reached about these distributions: the identification problem that arises from student non-participation during data collection. Recognizing that ILSA…

基于统计决策理论的预测算法全面样本外评价AI 解读

We argue that comprehensive out-of-sample (OOS) evaluation using statistical decision theory (SDT) should replace the current practice of K-fold and Common Task Framework validation in machine learning (ML) research on prediction. SDT provides a formal frequentist framework for performing comprehensive OOS evaluation across all possible (1) training samples, (2) populations that may generate training data, and (3) populations of prediction interest. Regarding feature (3), we…

用总误差幅度衡量选举民调的非抽样误差：无应答情形AI 解读

The potential impact of non-sampling errors on election polls is well known, but measurement has focused on the margin of sampling error. Survey statisticians have long recommended measurement of total survey error by mean square error (MSE), which jointly measures sampling and non-sampling errors. We think it reasonable to use the square root of maximum MSE to measure the total margin of error (TME). Measurement of TME should encompass both sampling error and all forms of…