混合效应模型中多宇宙分析的选择后推断（PIMAX）
Post-Selection Inference for Multiverse Analysis in Mixed-Effects Models (PIMAX)
AI总结：
结合两种方法开发PIMAX框架，用于聚类观测的多宇宙推断，将选择后推断扩展到聚类数据设置，能处理异方差等情况，提供全局p值等多种推断结果。
摘要：
符号翻转分数检验在方差错误指定下为广义线性模型提供稳健推断，是两种近期推断框架的基础。本文结合这两种方法开发PIMAX，将选择后推断扩展到聚类数据设置，避免基于完全指定的随机效应协方差结构的推断，解决I型错误膨胀问题。
英文摘要：
Sign-flipping score tests provide robust inference in generalized linear models under variance misspecification and form the basis of two recent inferential frameworks: post-selection inference in multiverse analysis (PIMA) and the sign-flipping score-based two-stage summary-statistics approach (flip2sss). PIMA provides asymptotically valid inference across a multiverse of model specifications, whereas flip2sss extends sign-flipping score testing to longitudinal and hierarchical data through cluster-level summary statistics. In this paper, we combine these two approaches to develop PIMAX, a multiverse inferential framework for clustered observations. The resulting method extends post-selection inference to clustered-data settings, accommodating heteroscedasticity, unbalanced designs, and within-cluster dependence. Given a multiverse of candidate specifications, PIMAX provides a global p-value for testing whether any specification exhibits a non-zero effect (weak control of the family-wise error rate, FWER), lower confidence bounds on the number of true discoveries, and multiplicity-adjusted p-values for identifying the specific contributing specifications (strong FWER control). By avoiding inference based on a fully specified random-effects covariance structure, PIMAX solves a key source of type I error inflation due to random-effects misspecification while enabling inference across a multiverse of fixed-effects specifications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
