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Tree-Guided Rare Feature Selection and Logic Aggregation with Electronic Health Records Data J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-03-07 Jianmin Chen, Robert H. Aseltine, Fei Wang, Kun Chen
Statistical learning with a large number of rare binary features is commonly encountered in analyzing electronic health records (EHR) data, especially in the modeling of disease onset with prior me...
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Martingale Methods in Statistics J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-03-05 Insuk Seo
Published in Journal of the American Statistical Association (Vol. 119, No. 545, 2024)
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The Journal of the American Statistical Association 2023 Associate Editors J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-03-05
Published in Journal of the American Statistical Association (Vol. 119, No. 545, 2024)
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Robust Personalized Federated Learning with Sparse Penalization* J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-02-23 Weidong Liu, Xiaojun Mao, Xiaofei Zhang, Xin Zhang
Federated learning (FL) is an emerging topic due to its advantage in collaborative learning with distributed data. Due to the heterogeneity in the local data-generating mechanism, it is important t...
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Doubly Flexible Estimation under Label Shift J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-02-21 Seong-ho Lee, Yanyuan Ma, Jiwei Zhao
In studies ranging from clinical medicine to policy research, complete data are usually available from a population P , but the quantity of interest is often sought for a related but different popu...
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Modeling Recurrent Failures on Large Directed Networks J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-02-15 Qingqing Zhai, Zhisheng Ye, Cheng Li, Matthew Revie, David B. Dunson
Many lifeline infrastructure systems consist of thousands of components configured in a complex directed network. Disruption of the infrastructure constitutes a recurrent failure process over a dir...
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Addressing Multiple Detection Limits with Semiparametric Cumulative Probability Models J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-02-13 Yuqi Tian, Chun Li, Shengxin Tu, Nathan T. James, Frank E. Harrell, Bryan E. Shepherd
Detection limits (DLs), where a variable cannot be measured outside of a certain range, are common in research. DLs may vary across study sites or over time. Most approaches to handling DLs in resp...
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Ranking Inferences Based on the Top Choice of Multiway Comparisons J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-02-09 Jianqing Fan, Zhipeng Lou, Weichen Wang, Mengxin Yu
Motivated by many applications such as online recommendations and individual choices, this paper considers ranking inference of n items based on the observed data on the top choice among M randomly...
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Operator-Induced Structural Variable Selection for Identifying Materials Genes J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-02-12 Shengbin Ye, Thomas P. Senftle, Meng Li
In the emerging field of materials informatics, a fundamental task is to identify physicochemically meaningful descriptors, or materials genes, which are engineered from primary features and a set ...
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Confidence Intervals for Parameters of Unobserved Events J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-02-07 Amichai Painsky
Consider a finite sample from an unknown distribution over a countable alphabet. Unobserved events are alphabet symbols which do not appear in the sample. Estimating the probabilities of unobserved...
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Evaluating Dynamic Conditional Quantile Treatment Effects with Applications in Ridesharing J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-02-06 Ting Li, Chengchun Shi, Zhaohua Lu, Yi Li, Hongtu Zhu
Many modern tech companies, such as Google, Uber, and Didi, utilize online experiments (also known as A/B testing) to evaluate new policies against existing ones. While most studies concentrate on ...
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Federated Offline Reinforcement Learning J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-02-02 Doudou Zhou, Yufeng Zhang, Aaron Sonabend-W, Zhaoran Wang, Junwei Lu, Tianxi Cai
Evidence-based or data-driven dynamic treatment regimes are essential for personalized medicine, which can benefit from offline reinforcement learning (RL). Although massive healthcare data are ava...
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Theory of Statistical Inference. J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-02-05 Somabha Mukherjee
Published in Journal of the American Statistical Association (Just accepted, 2024)
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Assessing COVID-19 Prevalence in Austria with Infection Surveys and Case Count Data as Auxiliary Information J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-02-05 Stéphane Guerrier, Christoph Kuzmics, Maria-Pia Victoria-Feser
Countries officially record the number of COVID-19 cases based on medical tests of a subset of the population. These case count data obviously suffer from participation bias, and for prevalence est...
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Heterogeneity Analysis on Multi-state Brain Functional Connectivity and Adolescent Neurocognition J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-02-01 Shiying Wang, Todd Constable, Heping Zhang, Yize Zhao
Brain functional connectivity or connectome, a unique measure for brain functional organization, provides a great potential to explain the neurobiological underpinning of behavioral profiles. Exist...
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An Interpretable and Efficient Infinite-Order Vector Autoregressive Model for High-Dimensional Time Series J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-01-31 Yao Zheng
As a special infinite-order vector autoregressive (VAR) model, the vector autoregressive moving average (VARMA) model can capture much richer temporal patterns than the widely used finite-order VAR...
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Stochastic Low-rank Tensor Bandits for Multi-dimensional Online Decision Making J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-01-30 Jie Zhou, Botao Hao, Zheng Wen, Jingfei Zhang, Will Wei Sun
Multi-dimensional online decision making plays a crucial role in many real applications such as online recommendation and digital marketing. In these problems, a decision at each time is a combinat...
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Model-Free Statistical Inference on High-Dimensional Data* J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-01-26 Xu Guo, Runze Li, Zhe Zhang, Changliang Zou
This paper aims to develop an effective model-free inference procedure for high-dimensional data. We first reformulate the hypothesis testing problem via sufficient dimension reduction framework. W...
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Consistent community detection in inter-layer dependent multi-layer networks J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-01-26 Jingnan Zhang, Junhui Wang, Xueqin Wang
Community detection in multi-layer networks, which aims at finding groups of nodes with similar connective patterns among all layers, has attracted tremendous interests in multi-layer network analy...
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Structural Equation Modeling Using R/SAS: A Step-by-Step Approach with Real Data Analysis. J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-01-24 Lifeng Lin
Published in Journal of the American Statistical Association (Just accepted, 2024)
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Mathematical Foundations of Infinite-Dimensional Statistical Models. J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-01-24 Bodhisattva Sen
Published in Journal of the American Statistical Association (Just accepted, 2024)
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Semiparametric Bayesian inference for local extrema of functions in the presence of noise J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-01-23 Meng Li, Zejian Liu, Cheng-Han Yu, Marina Vannucci
There is a wide range of applications where the local extrema of a function are the key quantity of interest. However, there is surprisingly little work on methods to infer local extrema with uncer...
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Markov bases: a 25 year update J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-01-26 Félix Almendra-Hernández, Jesús A. DeLoera, Sonja Petrović
In this paper, we evaluate the challenges and best practices associated with the Markov bases approach to sampling from conditional distributions. We provide insights and clarifications after 25 ye...
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Bayesian Integrative Region Segmentation in Spatially Resolved Transcriptomic Studies J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-01-22 Yinqiao Yan, Xiangyu Luo
The spatially resolved transcriptomic study is a recently developed biological experiment that can measure gene expressions and retain spatial information simultaneously, opening a new avenue to ch...
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Reinforcement Learning in Latent Heterogeneous Environments J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-01-22 Elynn Y. Chen, Rui Song, Michael I. Jordan
Reinforcement Learning holds great promise for data-driven decision-making in various social contexts, including healthcare, education, and business. However, classical methods that focus on the me...
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Leveraging Weather Dynamics in Insurance Claims Triage Using Deep Learning J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-01-22 Peng Shi, Wei Zhang, Kun Shi
In property insurance claims triage, insurers often use static information to assess the severity of a claim and to identify the subsequent actions. We hypothesize that the pattern of weather condi...
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Controlled Epidemiological Studies. J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-01-16 Kaushik Ghosh
Published in Journal of the American Statistical Association (Just accepted, 2024)
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Identifiability and Consistent Estimation for Gaussian Chain Graph Models J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-01-12 Ruixuan Zhao, Haoran Zhang, Junhui Wang
The chain graph model admits both undirected and directed edges in one graph, where symmetric conditional dependencies are encoded via undirected edges and asymmetric causal relations are encoded v...
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Weighted Functional Data Analysis for the Calibration of a Ground Motion Model in Italy J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-01-08 Teresa Bortolotti, Riccardo Peli, Giovanni Lanzano, Sara Sgobba, Alessandra Menafoglio
Motivated by the crucial implications of Ground Motion Models in terms of seismic hazard analysis and civil protection planning, this work extends a scalar Ground Motion Model for Italy to the fram...
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Data Science and Predictive Analytics: Biomedical and Health Applications using R, 2nd ed. J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-01-09 Xing Qiu
Published in Journal of the American Statistical Association (Just accepted, 2024)
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Statistical Modeling with R: A Dual Frequentist and Bayesian Approach for Life Scientists. J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-01-08 Christian P. Robert
Published in Journal of the American Statistical Association (Just accepted, 2024)
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Handbook of Matching and Weighting Adjustments for Causal Inference J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-01-09 Raymond K. W. Wong
Published in Journal of the American Statistical Association (Vol. 119, No. 545, 2024)
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A Composite Likelihood-based Approach for Change-point Detection in Spatio-temporal Processes J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-01-08 Zifeng Zhao, Ting Fung Ma, Wai Leong Ng, Chun Yip Yau
This paper develops a unified and computationally efficient method for change-point estimation along the time dimension in a non-stationary spatio-temporal process. By modeling a non-stationary spa...
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Graphical Principal Component Analysis of Multivariate Functional Time Series J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-01-08 Jianbin Tan, Decai Liang, Yongtao Guan, Hui Huang
In this paper, we consider multivariate functional time series with a two-way dependence structure: a serial dependence across time points and a graphical interaction among the multiple functions w...
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Quantitative Methods for Precision Medicine: Pharmacogenomics in Action. J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-01-08 Arthur Berg
Published in Journal of the American Statistical Association (Just accepted, 2024)
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Extremal Random Forests J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-01-08 Nicola Gnecco, Edossa Merga Terefe, Sebastian Engelke
Classical methods for quantile regression fail in cases where the quantile of interest is extreme and only few or no training data points exceed it. Asymptotic results from extreme value theory can...
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Policy Learning with Asymmetric Counterfactual Utilities* J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-01-08 Eli Ben-Michael, Kosuke Imai, Zhichao Jiang
Data-driven decision making plays an important role even in high stakes settings like medicine and public policy. Learning optimal policies from observed data requires a careful formulation of the ...
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Bayesian Lesion Estimation with a Structured Spike-and-Slab Prior J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-01-08 Anna Menacher, Thomas E. Nichols, Chris Holmes, Habib Ganjgahi
Neural demyelination and brain damage accumulated in white matter appear as hyperintense areas on T2-weighted MRI scans in the form of lesions. Modeling binary images at the population level, where...
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Fundamentals of Causal Inference: With R J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-01-05 Ting Ye
Published in Journal of the American Statistical Association (Vol. 119, No. 545, 2024)
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Stable Lévy Processes via Lamperti-Type Representations J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-01-05 Giacomo Bormetti
Published in Journal of the American Statistical Association (Vol. 119, No. 545, 2024)
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Recommender Systems: A Review J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-01-04 Patrick M. LeBlanc, David Banks, Linhui Fu, Mingyan Li, Zhengyu Tang, Qiuyi Wu
Recommender systems are the engine of online advertising. Not only do they suggest movies, music, or romantic partners, but they also are used to select which advertisements to show to users. This ...
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Clustering High-Dimensional Noisy Categorical Data J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-01-02 Zhiyi Tian, Jiaming Xu, Jen Tang
Clustering is a widely used unsupervised learning technique that groups data into homogeneous clusters. However, when dealing with real-world data that contain categorical values, existing algorith...
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Confidence Intervals for Discrete Data in Clinical Research J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2023-12-29 Alan Agresti
Published in Journal of the American Statistical Association (Vol. 118, No. 544, 2023)
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Robust Validation: Confident Predictions Even When Distributions Shift* J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2023-12-26 Maxime Cauchois, Suyash Gupta, Alnur Ali, John C. Duchi
While the traditional viewpoint in machine learning and statistics assumes training and testing samples come from the same population, practice belies this fiction. One strategy—coming from robust ...
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Rejoinder to Discussion of “A Tale of Two Datasets: Representativeness and Generalisability of Inference for Samples of Networks” J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2023-12-29 Pavel N. Krivitsky, Pietro Coletti, Niel Hens
Published in Journal of the American Statistical Association (Vol. 118, No. 544, 2023)
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Discussion of “A Tale of Two Datasets: Representativeness and Generalisability of Inference for Samples of Networks” J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2023-12-29 Nynke M. D. Niezink
Published in Journal of the American Statistical Association (Vol. 118, No. 544, 2023)
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Power and Multicollinearity in Small Networks: A Discussion of “Tale of Two Datasets: Representativeness and Generalisability of Inference for Samples of Networks” by Krivitsky, Coletti, and Hens J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2023-12-29 George G. Vega Yon
Published in Journal of the American Statistical Association (Vol. 118, No. 544, 2023)
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A Unified Nonparametric Fiducial Approach to Interval-Censored Data J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2023-12-22 Yifan Cui, Jan Hannig, Michael R. Kosorok
Censored data, where the event time is partially observed, are challenging for survival probability estimation. In this article, we introduce a novel nonparametric fiducial approach to interval-cen...
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A Kernel Measure of Dissimilarity between M Distributions J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2023-12-21 Zhen Huang, Bodhisattva Sen
Given M≥2 distributions defined on a general measurable space, we introduce a nonparametric (kernel) measure of multi-sample dissimilarity (KMD) — a parameter that quantifies the difference betwee...
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Estimating cell-type-specific gene co-expression networks from bulk gene expression data with an application to Alzheimer’s disease J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2023-12-21 Chang Su, Jingfei Zhang, Hongyu Zhao
Inferring and characterizing gene co-expression networks has led to important insights on the molecular mechanisms of complex diseases. Most co-expression analyses to date have been performed on ge...
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Dynamic Matrix Recovery J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2023-12-21 Ziyuan Chen, Ying Yang, Fang Yao
Matrix recovery from sparse observations is an extensively studied topic emerging in various applications, such as recommendation system and signal processing, which includes the matrix completion ...
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Bayesian Landmark-based Shape Analysis of Tumor Pathology Images J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2023-12-21 Cong Zhang, Tejasv Bedi, Chul Moon, Yang Xie, Min Chen, Qiwei Li
Medical imaging is a form of technology that has revolutionized the medical field over the past decades. Digital pathology imaging, which captures histological details at the cellular level, is rap...
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Fitting latent non-Gaussian models using variational Bayes and Laplace approximations J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2023-12-21 Rafael Cabral, David Bolin, Håvard Rue
Abstract–Latent Gaussian models (LGMs) are perhaps the most commonly used class of models in statistical applications. Nevertheless, in areas ranging from longitudinal studies in biostatistics to g...
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Online Statistical Inference for Stochastic Optimization via Kiefer-Wolfowitz Methods J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2023-12-21 Xi Chen, Zehua Lai, He Li, Yichen Zhang
This paper investigates the problem of online statistical inference of model parameters in stochastic optimization problems via the Kiefer-Wolfowitz algorithm with random search directions. We firs...
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Large Scale Prediction with Decision Trees J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2023-12-18 Jason M. Klusowski, Peter M. Tian
This article shows that decision trees constructed with Classification and Regression Trees (CART) and C4.5 methodology are consistent for regression and classification tasks, even when the number ...
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A Feasibility Study of Differentially Private Summary Statistics and Regression Analyses with Evaluations on Administrative and Survey Data J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2023-12-11 Andrés F. Barrientos, Aaron R. Williams, Joshua Snoke, Claire McKay Bowen
Federal administrative data, such as tax data, are invaluable for research, but because of privacy concerns, access to these data is typically limited to select agencies and a few individuals. An a...
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Statistical Analytics for Health Data Science with SAS and R J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2023-12-06 Ali Rahnavard
Published in Journal of the American Statistical Association (Vol. 119, No. 545, 2024)
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Model-robust and efficient covariate adjustment for cluster-randomized experiments J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2023-11-30 Bingkai Wang, Chan Park, Dylan S. Small, Fan Li
Cluster-randomized experiments are increasingly used to evaluate interventions in routine practice conditions, and researchers often adopt model-based methods with covariate adjustment in the stati...
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Bounding Wasserstein distance with couplings J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2023-11-27 Niloy Biswas, Lester Mackey
Markov chain Monte Carlo (MCMC) provides asymptotically consistent estimates of intractable posterior expectations as the number of iterations tends to infinity. However, in large data applications...
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Balancing Covariates in Randomized Experiments with the Gram–Schmidt Walk Design J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2023-11-21 Christopher Harshaw, Fredrik Sävje, Daniel A. Spielman, Peng Zhang
The design of experiments involves a compromise between covariate balance and robustness. This paper provides a formalization of this trade-off and describes an experimental design that allows expe...