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利用再现样本进行高维线性回归模型与系数的有限样本及大样本推断AI 解读

In this paper, we present a novel and effective inference approach to conduct both finite and large sample inference for high-dimensional linear regression models. This approach is developed under the so-called repro samples framework, in which we conduct statistical inference by creating and studying the behavior of artificial samples that are obtained by mimicking the sampling mechanism of the data. We construct confidence sets for (a) the true model corresponding to the…

个体化群组学习AI 解读

Many massive data sets are assembled through collections of information of a large number of individuals in a population. The analysis of such data, especially in the aspect of individualized inferences and solutions, has the potential to create significant value for practical applications. Traditionally, inference for an individual in the dataset is either solely relying on the information of the individual or from summarizing the information about the whole population.…

多参数非参数融合学习：结合数据深度与置信分布AI 解读

Fusion learning refers to synthesizing inferences from multiple sources or studies to make a more effective inference and prediction than from any individual source or study alone. Most existing methods for synthesizing inferences rely on parametric model assumptions, such as normality, which often do not hold in practice. We propose a general nonparametric fusion learning framework for synthesizing inferences for multiparameters from different studies. The main tool…

iFusion：个体化融合学习AI 解读

Inferences from different data sources can often be fused together, a process referred to as “fusion learning,” to yield more powerful findings than those from individual data sources alone. Effective fusion learning approaches are in growing demand as increasing number of data sources have become easily available in this big data era. This article proposes a new fusion learning approach, called “iFusion,” for drawing efficient individualized inference by fusing learnings…

对 Bradley Efron《预测、估计与归因》一文的讨论AI 解读

By noting the rapid growing trend of “pure prediction algorithms,” Professor Efron compares and bridges the statistics of the 20th Century (estimation and attribution) to that of the current fast g...

离散数据的精确荟萃分析及其稀有事件列联表应用AI 解读

) associated with the exact tests from individual studies. It encompasses a broad class of exact meta-analysis methods, as it permits broad choices for the combining elements, such as tests used in individual studies, and any parameter of interest. The approach yields statements that explicitly account for the impact of individual studies on the overall inference, in terms of efficiency/power and the type I error rate. Those statements also give rises to empirical methods…