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Matrix Completion When Missing Is Not at Random and Its Applications in Causal Panel Data Models* J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-07-17 Jungjun Choi, Ming Yuan
This paper develops an inferential framework for matrix completion when missing is not at random and without the requirement of strong signals. Our development is based on the observation that if t...
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Controlling the False Split Rate in Tree-Based Aggregation J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-07-09 Simeng Shao, Jacob Bien, Adel Javanmard
In many domains, data measurements can naturally be associated with the leaves of a tree, expressing the relationships among these measurements. For example, companies belong to industries, which i...
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Sparse Graphical Modeling for High Dimensional Data: A Paradigm of Conditional Independence Tests J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-07-08 Reza Mohammadi
Published in Journal of the American Statistical Association (Just accepted, 2024)
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Robust Matrix Completion with Heavy-tailed Noise J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-07-03 Bingyan Wang, Jianqing Fan
This paper studies noisy low-rank matrix completion in the presence of heavy-tailed and possibly asymmetric noise, where we aim to estimate an underlying low-rank matrix given a set of highly incom...
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Sparse Independent Component Analysis with an Application to Cortical Surface fMRI Data in Autism J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-06-26 Zihang Wang, Irina Gaynanova, Aleksandr Aravkin, Benjamin B. Risk
Independent component analysis (ICA) is widely used to estimate spatial resting-state networks and their time courses in neuroimaging studies. It is thought that independent components correspond t...
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Statistical Inference for Hüsler–Reiss Graphical Models Through Matrix Completions J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-06-26 Manuel Hentschel, Sebastian Engelke, Johan Segers
The severity of multivariate extreme events is driven by the dependence between the largest marginal observations. The Hüsler–Reiss distribution is a versatile model for this extremal dependence, a...
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Contextual Dynamic Pricing with Strategic Buyers J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-06-26 Pangpang Liu, Zhuoran Yang, Zhaoran Wang, Will Wei Sun
Personalized pricing, which involves tailoring prices based on individual characteristics, is commonly used by firms to implement a consumer-specific pricing policy. In this process, buyers can als...
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Supervised Dynamic PCA: Linear Dynamic Forecasting with Many Predictors J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-06-24 Zhaoxing Gao, Ruey S. Tsay
This paper proposes a novel dynamic forecasting method using a new supervised Principal Component Analysis (PCA) when a large number of predictors are available. The new supervised PCA provides an ...
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Synthetic likelihood in misspecified models J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-06-24 David T. Frazier, David J. Nott, Christopher Drovandi
Bayesian synthetic likelihood is a widely used approach for conducting Bayesian analysis in complex models where evaluation of the likelihood is infeasible but simulation from the assumed model is ...
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Corrigendum to Maximum Likelihood Estimation of the Multivariate Normal Mixture Model J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-06-21
Published in Journal of the American Statistical Association (Just accepted, 2024)
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Enhanced Response Envelope via Envelope Regularization J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-06-18 Oh-Ran Kwon, Hui Zou
The response envelope model provides substantial efficiency gains over the standard multivariate linear regression by identifying the material part of the response to the model and by excluding the...
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Generalized Data Thinning Using Sufficient Statistics J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-06-13 Ameer Dharamshi, Anna Neufeld, Keshav Motwani, Lucy L. Gao, Daniela Witten, Jacob Bien
Our goal is to develop a general strategy to decompose a random variable X into multiple independent random variables, without sacrificing any information about unknown parameters. A recent paper s...
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Tyranny-of-the-minority Regression Adjustment in Randomized Experiments J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-06-12 Xin Lu, Hanzhong Liu
Abstract–Regression adjustment is widely used in the analysis of randomized experiments to improve the estimation efficiency of the treatment effect. This paper reexamines a weighted regression adj...
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Test and Measure for Partial Mean Dependence Based on Machine Learning Methods J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-06-11 Leheng Cai, Xu Guo, Wei Zhong
It is of importance to investigate the significance of a subset of covariates W for the response Y given covariates Z in regression modeling. To this end, we propose a significance test for the par...
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Nonparametric Multiple-Output Center-Outward Quantile Regression J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-06-11 Eustasio del Barrio, Alberto González Sanz, Marc Hallin
Building on recent measure-transportation-based concepts of multivariate quantiles, we are considering the problem of nonparametric multiple-output quantile regression. Our approach defines nested ...
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Correction J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-06-03 Pavel N. Krivitsky, Pietro Coletti, Niel Hens
This note provides correction to some numerical results in Krivitsky P. N., Coletti, P., and Hens, N. (2023), “A Tale of Two Datasets: Representativeness and Generalisability of Inference for Sampl...
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Randomness of Shapes and Statistical Inference on Shapes via the Smooth Euler Characteristic Transform J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-05-31 Kun Meng, Jinyu Wang, Lorin Crawford, Ani Eloyan
In this article, we establish the mathematical foundations for modeling the randomness of shapes and conducting statistical inference on shapes using the smooth Euler characteristic transform. Base...
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Graph-Aligned Random Partition Model (GARP) J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-05-30 Giovanni Rebaudo, Peter Müller
Bayesian nonparametric mixtures and random partition models are powerful tools for probabilistic clustering. However, standard independent mixture models can be restrictive in some applications suc...
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Efficient stochastic generators with spherical harmonic transformation for high-resolution global climate simulations from CESM2-LENS2 J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-05-29 Yan Song, Zubair Khalid, Marc G. Genton
Earth system models (ESMs) are fundamental for understanding Earth’s complex climate system. However, the computational demands and storage requirements of ESM simulations limit their utility. For ...
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Generalizing the intention-to-treat effect of an active control from historical placebo-controlled trials: A case study of the efficacy of daily oral TDF/FTC in the HPTN 084 study J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-05-29 Qijia He, Fei Gao, Oliver Dukes, Sinead Delany-Moretlwe, Bo Zhang
In many clinical settings, an active-controlled trial design (e.g., a non-inferiority or superiority design) is often used to compare an experimental medicine to an active control (e.g., an FDA-app...
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False Discovery Rate Control For Structured Multiple Testing: Asymmetric Rules And Conformal Q-values J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-05-28 Zinan Zhao, Wenguang Sun
The effective utilization of structural information in data while ensuring statistical validity poses a significant challenge in false discovery rate (FDR) analyses. Conformal inference provides ri...
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Node-level community detection within edge exchangeable models for interaction processes J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-05-28 Yuhua Zhang, Walter Dempsey
Scientists are increasingly interested in discovering community structure from modern relational data arising on large-scale social networks. While many methods have been proposed for learning comm...
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Mediation analysis with the mediator and outcome missing not at random J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-05-28 Shuozhi Zuo, Debashis Ghosh, Peng Ding, Fan Yang
Mediation analysis is widely used for investigating direct and indirect causal pathways through which an effect arises. However, many mediation analysis studies are challenged by missingness in the...
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Distributed Heterogeneity Learning for Generalized Partially Linear Models with Spatially Varying Coefficients1 J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-05-24 Shan Yu, Guannan Wang, Li Wang
Spatial heterogeneity is of great importance in social, economic, and environmental science studies. The spatially varying coefficient model is a popular and effective spatial regression technique ...
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Doubly Robust Augmented Model Accuracy Transfer Inference with High Dimensional Features J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-05-21 Doudou Zhou, Molei Liu, Mengyan Li, Tianxi Cai
Transfer learning is crucial for training models that generalize to unlabeled target populations using labeled source data, especially in real-world studies where label scarcity and covariate shift...
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Joint tensor modeling of single cell 3D genome and epigenetic data with Muscle J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-05-21 Kwangmoon Park, Sündüz Keleş
Emerging single cell technologies that simultaneously capture long-range interactions of genomic loci together with their DNA methylation levels are advancing our understanding of three-dimensional...
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Population-level Balance in Signed Networks J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-05-21 Weijing Tang, Ji Zhu
Statistical network models are useful for understanding the underlying formation mechanism and characteristics of complex networks. However, statistical models for signed networks have been largely...
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Estimating Trans-Ancestry Genetic Correlation with Unbalanced Data Resources J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-05-21 Bingxin Zhao, Xiaochen Yang, Hongtu Zhu
The aim of this article is to propose a novel method for estimating trans-ancestry genetic correlations in genome-wide association studies (GWAS) using genetically predicted observations. These cor...
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Rational Kriging J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-05-20 V. Roshan Joseph
This article proposes a new kriging that has a rational form. It is shown that the generalized least squares estimator of the mean from rational kriging is much more well behaved than that of ordin...
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Distribution-Free Prediction Intervals Under Covariate Shift, With an Application to Causal Inference J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-05-20 Jing Qin, Yukun Liu, Moming Li, Chiung-Yu Huang
Owing to its appealing distribution-free feature, conformal inference has become a popular tool for constructing prediction intervals with a desired coverage rate. In scenarios involving covariate ...
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Neural networks for geospatial data J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-05-20 Wentao Zhan, Abhirup Datta
Abstract–Analysis of geospatial data has traditionally been model-based, with a mean model, customarily specified as a linear regression on the covariates, and a Gaussian process covariance model, ...
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Tests for large-dimensional shape matrices via Tyler’s M estimators J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-05-03 Runze Li, Weiming Li, Qinwen Wang
Tyler’s M estimator, as a robust alternative to the sample covariance matrix, has been widely applied in robust statistics. However, classical theory on Tyler’s M estimator is mainly developed in t...
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Controlled Discovery and Localization of Signals via Bayesian Linear Programming J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-04-26 Asher Spector, Lucas Janson
Scientists often must simultaneously localize and discover signals. For instance, in genetic fine-mapping, high correlations between nearby genetic variants make it hard to identify the exact locat...
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Partnering With Authors to Enhance Reproducibility at JASA J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-04-25 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 (Vol. 119, No. 546, 2024)
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Testing the number of common factors by bootstrapped sample covariance matrix in high-dimensional factor models J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-04-22 Long Yu, Peng Zhao, Wang Zhou
This paper studies the impact of bootstrap procedure on the eigenvalue distributions of the sample covariance matrix under a high-dimensional factor structure. We provide asymptotic distributions f...
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Modeling and Learning on High-Dimensional Matrix-Variate Sequences J. Am. Stat. Assoc. (IF 3.0) 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 3.0) 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 3.0) 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 3.0) 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 3.0) 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 3.0) 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 3.0) 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 3.0) 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 3.0) 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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Generalized Bayesian Additive Regression Trees Models: Beyond Conditional Conjugacy J. Am. Stat. Assoc. (IF 3.0) 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 3.0) 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 3.0) 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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Addressing Multiple Detection Limits with Semiparametric Cumulative Probability Models J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-04-01 Yuqi Tian, Chun Li, Shengxin Tu, Nathan T. James, FrankE. Harrell, BryanE. 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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Mathematical Foundations of Infinite-Dimensional Statistical Models J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-04-01 Bodhisattva Sen
Published in Journal of the American Statistical Association (Vol. 119, No. 546, 2024)
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Statistical Inference For Noisy Matrix Completion Incorporating Auxiliary Information* J. Am. Stat. Assoc. (IF 3.0) 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 3.0) 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 3.0) 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 3.0) 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 3.0) 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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Heterogeneity Analysis on Multi-State Brain Functional Connectivity and Adolescent Neurocognition J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-03-08 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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Markov Bases: A 25 Year Update J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-03-08 Félix Almendra-Hernández, Jesús A. De Loera, Sonja Petrović
In this article, 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 ...
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Theory of Statistical Inference J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-03-08 Somabha Mukherjee
Published in Journal of the American Statistical Association (Vol. 119, No. 546, 2024)
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Tree-Guided Rare Feature Selection and Logic Aggregation with Electronic Health Records Data J. Am. Stat. Assoc. (IF 3.0) 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 3.0) Pub Date : 2024-03-05 Insuk Seo
Published in Journal of the American Statistical Association (Vol. 119, No. 545, 2024)
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Leveraging Weather Dynamics in Insurance Claims Triage Using Deep Learning J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-03-06 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...