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广义线性模型及其扩展的统一残差诊断框架AI 解读

Model diagnostics is an indispensable component in regression analysis, yet it has not been well addressed in generalized linear models (GLMs). When outcome data are discrete, classical Pearson and deviance residuals have limited utility in generating diagnostic insights. This article establishes a novel diagnostic framework for GLMs and their extensions. Unlike the convention of using a point statistic as a residual, we propose to use a function as a vehicle to retain…

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…

有序变量间偏关联的量化、可视化与假设检验AI 解读

Partial association refers to the relationship between variables Y1,Y2,…,YK while adjusting for a set of covariates X={X1,…,Xp}. To assess such an association when Yk’s are recorded on ordinal scales, a classical approach is to use partial correlation between the latent continuous variables. This so-called polychoric correlation is inadequate, as it requires multivariate normality and it only reflects a linear association. We propose a new framework for studying…

有序回归模型的残差与诊断：替代变量方法AI 解读

Ordinal outcomes are common in scientific research and everyday practice, and we often rely on regression models to make inference. A long-standing problem with such regression analyses is the lack of effective diagnostic tools for validating model assumptions. The difficulty arises from the fact that an ordinal variable has discrete values that are labeled with, but not, numerical values. The values merely represent ordered categories. In this article, we propose a…

仅用汇总统计量的异质研究多变量元分析：效率与稳健性AI 解读

Meta-analysis has been widely used to synthesize evidence from multiple studies for common hypotheses or parameters of interest. However, it has not yet been fully developed for incorporating heterogeneous studies, which arise often in applications due to different study designs, populations, or outcomes. For heterogeneous studies, the parameter of interest may not be estimable for certain studies, and in such a case, these studies are typically excluded from conventional…

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…