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作者：Liu, Wei; Lin, Huazhen; Zheng, Shurong; Liu, Jin
作者单位：Southwestern University of Finance & Economics - China; Northeast Normal University - China; National University of Singapore
摘要：As high-dimensional data measured with mixed-type variables gradually become prevalent, it is particularly appealing to represent those mixed-type high-dimensional data using a much smaller set of so-called factors. Due to the limitation of the existing methods for factor analysis that deal with only continuous variables, in this article, we develop a generalized factor model, a corresponding algorithm and theory for ultra-high dimensional mixed types of variables where both the sample size n ...
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作者：Hung, Ying; Lin, Li-Hsiang; Wu, C. F. Jeff
作者单位：Rutgers University System; Rutgers University Newark; Rutgers University New Brunswick; Louisiana State University System; Louisiana State University; University System of Georgia; Georgia Institute of Technology
摘要：Computer simulators are widely used for the study of complex systems. In many applications, there are multiple simulators available with different scientific interpretations of the underlying mechanism, and the goal is to identify an optimal simulator based on the observed physical experiments. To achieve the goal, we propose a selection criterion based on leave-one-out cross-validation. This criterion consists of a goodness-of-fit measure and a generalized degrees of freedom penalizing the si...
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作者：Dagdoug, Mehdi; Goga, Camelia; Haziza, David
作者单位：Universite Marie et Louis Pasteur; University of Ottawa
摘要：In surveys, the interest lies in estimating finite population parameters such as population totals and means. In most surveys, some auxiliary information is available at the estimation stage. This information may be incorporated in the estimation procedures to increase their precision. In this article, we use random forests (RFs) to estimate the functional relationship between the survey variable and the auxiliary variables. In recent years, RFs have become attractive as National Statistical O...
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作者：Yang, Zihao; Qu, Tianyi; Li, Xinran
作者单位：University of Illinois System; University of Illinois Urbana-Champaign
摘要：Classical randomized experiments, equipped with randomization-based inference, provide assumption-free inference for treatment effects. They have been the gold standard for drawing causal inference and provide excellent internal validity. However, they have also been criticized for questionable external validity, in the sense that the conclusion may not generalize well to a larger population. The randomized survey experiment is a design tool that can help mitigate this concern, by randomly sel...
