惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

阮一峰的网络日志
阮一峰的网络日志
Last Week in AI
Last Week in AI
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
U
Unit 42
J
Java Code Geeks
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
罗磊的独立博客
月光博客
月光博客
腾讯CDC
Stack Overflow Blog
Stack Overflow Blog
小众软件
小众软件
B
Blog
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
美团技术团队
Y
Y Combinator Blog
T
Tailwind CSS Blog
宝玉的分享
宝玉的分享
酷 壳 – CoolShell
酷 壳 – CoolShell
博客园_首页
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
爱范儿
爱范儿
B
Blog RSS Feed
V
Visual Studio Blog
MyScale Blog
MyScale Blog

stat updates on arXiv.org

A Refined Generalization Analysis for Extreme Multi-class Supervised Contrastive Representation Learning Ensemble Distributionally Robust Bayesian Optimisation The Proxy Presumption: From Semantic Embeddings to Valid Social Measures Modulated learning for private and distributed regression with just a single sample per client device Query-efficient model evaluation using cached responses Functional-prior-based approaches to Bayesian PDE-constrained inversion using physics-informed neural networks Optimal Experiments for Partial Causal Effect Identification Order-Agnostic Autoregressive Modelling with Missing Data Grokking or Glitching? How Low-Precision Drives Slingshot Loss Spikes Tuning Derivatives for Causal Fairness in Machine Learning Spherical Flows for Sampling Categorical Data Bayesian Rain Field Reconstruction using Commercial Microwave Links and Diffusion Model Priors GRALIS: A Unified Canonical Framework for Linear Attribution Methods via Riesz Representation Sharp Capacity Thresholds in Linear Associative Memory: From Winner-Take-All to Listwise Retrieval Unified Framework of Distributional Regret in Multi-Armed Bandits and Reinforcement Learning Jacobian-Velocity Bounds for Deployment Risk Under Covariate Drift Self-Attention as Transport: Limits of Symmetric Spectral Diagnostics Perturbation is All You Need for Extrapolating Language Models Adapt or Forget: Provable Tradeoffs Between Adam and SGD in Nonstationary Optimization Realizable Bayes-Consistency for General Metric Losses Graph Convolutional Support Vector Regression for Robust Spatiotemporal Forecasting of Urban Air Pollution Segmenting Human-LLM Co-authored Text via Change Point Detection Stochastic Schrödinger Diffusion Models for Pure-State Ensemble Generation Understanding Self-Supervised Learning via Latent Distribution Matching The Geometric Mechanics of Contrastive Representation Learning: Alignment Potentials, Entropic Dispersion, and Cross-modal Divergence Imbalanced Classification under Capacity Constraints On the Spectral Structure and Objective Equivalence of Orthogonal Multilabel Fisher Discriminants Partially Observed Structural Causal Models First-Order Efficiency for Probabilistic Value Estimation via A Statistical Viewpoint Robust and Fast Training via Per-Sample Clipping
A generalized multiple-intervention stepped wedge design ...
[Submitted on 22 Jun 2026] · 2026-06-23 · via stat updates on arXiv.org

View PDF HTML (experimental)

Abstract:Existing power and design methods for multiple-intervention stepped wedge designs (M-SWDs) typically assume exchangeable cluster-period correlation, despite evidence that correlation often decays over time. Misspecification of this correlation structure can substantially distort variance estimation and power, particularly for treatment interaction effects. We develop a unified covariance framework for M-SWDs that separates intracluster correlation from an explicit cluster-period correlation matrix. This formulation accommodates exchangeable, autoregressive, and more general distance-dependent correlation structures while preserving closed-form expressions for the variance of treatment effect estimators under linear mixed models. Using analytic results and simulation studies, we demonstrate that assuming uniform correlation when the true structure is time-dependent can lead to substantial power mischaracterization. Specifically, we find that designs calibrated under independence assumptions may be overly conservative and compound symmetry can be either optimistic or conservative. These findings demonstrate the importance of explicitly modeling cluster-period correlation at the design stage of M-SWDs and provide practical guidance for power calculation and design selection in realistic settings.

Submission history

From: Jose-Miguel Yamal [view email]
[v1] Mon, 22 Jun 2026 15:46:01 UTC (7,501 KB)