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Estimating trans-ancestry genetic correlation with unbalanced data resources J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-04-19 Bingxin Zhao, Xiaochen Yang, Hongtu Zhu
The aim of this paper is to propose a novel method for estimating trans-ancestry genetic correlations in genome-wide association studies (GWAS) using genetically-predicted observations. These corre...
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Modeling and Learning on High-Dimensional Matrix-Variate Sequences J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-04-19 Xu Zhang, Catherine C. Liu, Jianhua Guo, K. C. Yuen, A. H. Welsh
We propose a new matrix factor model, named RaDFaM, which is strictly derived based on the general rank decomposition and assumes a structure of a high-dimensional vector factor model for each basi...
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Selection and Aggregation of Conformal Prediction Sets J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-04-17 Yachong Yang, Arun Kumar Kuchibhotla
Conformal prediction is a generic methodology for finite-sample valid distribution-free prediction. This technique has garnered a lot of attention in the literature partly because it can be applied...
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Statistical Methods in Health Disparity Research. J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-04-17 Susan M. Paddock
Published in Journal of the American Statistical Association (Just accepted, 2024)
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Introduction to Environmental Data Science J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-04-16 Timothée Poisot
Published in Journal of the American Statistical Association (Just accepted, 2024)
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Dissecting gene expression heterogeneity: generalized Pearson correlation squares and the K-lines clustering algorithm J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-04-15 Jingyi Jessica Li, Heather J. Zhou, Peter J. Bickel, Xin Tong
Motivated by the pressing needs for dissecting heterogeneous relationships in gene expression data, here we generalize the squared Pearson correlation to capture a mixture of linear dependences bet...
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Sharp-SSL: Selective high-dimensional axis-aligned random projections for semi-supervised learning J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-04-12 Tengyao Wang, Edgar Dobriban, Milana Gataric, Richard J. Samworth
We propose a new method for high-dimensional semi-supervised learning problems based on the careful aggregation of the results of a low-dimensional procedure applied to many axis-aligned random pro...
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Coexchangeable process modelling for uncertainty quantification in joint climate reconstruction J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-04-11 Lachlan Astfalck, Daniel Williamson, Niall Gandy, Lauren Gregoire, Ruza Ivanovic
Any experiment with climate models relies on a potentially large set of spatio-temporal boundary conditions. These can represent both the initial state of the system and/or forcings driving the mod...
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Sobolev Calibration of Imperfect Computer Models J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-04-09 Qingwen Zhang, Wenjia Wang
Calibration refers to the statistical estimation of unknown model parameters in computer experiments, such that computer experiments can match underlying physical systems. This work develops a new ...
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Efficient Nonparametric Estimation of Stochastic Policy Effects with Clustered Interference J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-04-09 Chanhwa Lee, Donglin Zeng, Michael G. Hudgens
Interference occurs when a unit’s treatment (or exposure) affects another unit’s outcome. In some settings, units may be grouped into clusters such that it is reasonable to assume that interference...
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Partnering with Authors to Enhance Reproducibility at JASA J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-04-08 Julia Wrobel, Emily C. Hector, Lorin Crawford, Lucy D’Agostino McGowan, Natalia da Silva, Jeff Goldsmith, Stephanie Hicks, Michael Kane, Youjin Lee, Vinicius Mayrink, Christopher J. Paciorek, Therri Usher, Julian Wolfson
Published in Journal of the American Statistical Association (Just accepted, 2024)
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Generalized Bayesian Additive Regression Trees Models: Beyond Conditional Conjugacy J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-04-05 Antonio R. Linero
Bayesian additive regression trees have seen increased interest in recent years due to their ability to combine machine learning techniques with principled uncertainty quantification. The Bayesian ...
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Inferring independent sets of Gaussian variables after thresholding correlations J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-04-05 Arkajyoti Saha, Daniela Witten, Jacob Bien
We consider testing whether a set of Gaussian variables, selected from the data, is independent of the remaining variables. This set is selected via a very simple approach: these are the variables ...
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Doubly-Valid/Doubly-Sharp Sensitivity Analysis for Causal Inference with Unmeasured Confounding J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-04-01 Jacob Dorn, Kevin Guo, Nathan Kallus
We consider the problem of constructing bounds on the average treatment effect (ATE) when unmeasured confounders exist but have bounded influence. Specifically, we assume that omitted confounders c...
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Statistical Inference For Noisy Matrix Completion Incorporating Auxiliary Information* J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-03-27 Shujie Ma, Po-Yao Niu, Yichong Zhang, Yinchu Zhu
This paper investigates statistical inference for noisy matrix completion in a semi-supervised model when auxiliary covariates are available. The model consists of two parts. One part is a low-rank...
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Automatic regenerative simulation via non-reversible simulated tempering J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-03-27 Miguel Biron-Lattes, Trevor Campbell, Alexandre Bouchard-Côté
Simulated Tempering (ST) is an MCMC algorithm for complex target distributions that operates on a path between the target and a more amenable reference distribution. Crucially, if the reference ena...
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CARE: Large Precision Matrix Estimation for Compositional Data J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-03-27 Shucong Zhang, Huiyuan Wang, Wei Lin
High-dimensional compositional data are prevalent in many applications. The simplex constraint poses intrinsic challenges to inferring the conditional dependence relationships among the components ...
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An efficient coalescent model for heterochronously sampled molecular data J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-03-20 Lorenzo Cappello, Amandine Véber, Julia A. Palacios
Molecular sequence variation at a locus informs about the evolutionary history of the sample and past population size dynamics. The Kingman coalescent is used in a generative model of molecular seq...
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Extreme value statistics in semi-supervised models J. Am. Stat. Assoc. (IF 3.7) Pub Date : 2024-03-21 Hanan Ahmed, John H.J. Einmahl, Chen Zhou
We consider extreme value analysis in a semi-supervised setting, where we observe, next to the n data on the target variable, n+m data on one or more covariates. This is called the semi-supervised...
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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 ...