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Robust estimation for number of factors in high dimensional factor modeling via Spearman correlation matrix J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-09-20 Jiaxin Qiu, Zeng Li, Jianfeng Yao
Determining the number of factors in high-dimensional factor modeling is essential but challenging, especially when the data are heavy-tailed. In this paper, we introduce a new estimator based on t...
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Bisection Grover’s Search Algorithm and Its Application in Analyzing CITE-seq Data J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-09-20 Ping Ma, Yongkai Chen, Haoran Lu, Wenxuan Zhong
With the rapid development of quantum computers, researchers have shown quantum advantages in physics-oriented problems. Quantum algorithms tackling computational biology problems are still lacking...
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Large-Scale Low-Rank Gaussian Process Prediction with Support Points J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-09-20 Yan Song, Wenlin Dai, Marc G. Genton
Low-rank approximation is a popular strategy to tackle the “big n problem” associated with large-scale Gaussian process regressions. Basis functions for developing low-rank structures are crucial a...
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Zigzag path connects two Monte Carlo samplers: Hamiltonian counterpart to a piecewise deterministic Markov process J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-09-18 Akihiko Nishimura, Zhenyu Zhang, Marc A. Suchard
Zigzag and other piecewise deterministic Markov process samplers have attracted significant interest for their non-reversibility and other appealing properties for Bayesian posterior computation. H...
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Valid Inference After Causal Discovery J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-09-17 Paula Gradu, Tijana Zrnic, Yixin Wang, Michael I. Jordan
Causal discovery and causal effect estimation are two fundamental tasks in causal inference. While many methods have been developed for each task individually, statistical challenges arise when app...
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Correction J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-09-13
Published in Journal of the American Statistical Association (Just accepted, 2024)
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Off-policy Evaluation in Doubly Inhomogeneous Environments J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-09-09 Zeyu Bian, Chengchun Shi, Zhengling Qi, Lan Wang
This work aims to study off-policy evaluation (OPE) under scenarios where two key reinforcement learning (RL) assumptions – temporal stationarity and individual homogeneity are both violated. To ha...
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Evaluating Treatment Prioritization Rules via Rank-Weighted Average Treatment Effects J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-09-03 Steve Yadlowsky, Scott Fleming, Nigam Shah, Emma Brunskill, Stefan Wager
There are a number of available methods for selecting whom to prioritize for treatment, including ones based on treatment effect estimation, risk scoring, and hand-crafted rules. We propose rank-we...
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Model-based causal feature selection for general response types J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-08-30 Lucas Kook, Sorawit Saengkyongam, Anton Rask Lundborg, Torsten Hothorn, Jonas Peters
Discovering causal relationships from observational data is a fundamental yet challenging task. Invariant causal prediction (ICP, Peters et al., 2016) is a method for causal feature selection which...
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Bayesian Structure Learning in Undirected Gaussian Graphical Models: Literature Review with Empirical Comparison J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-08-30 Lucas Vogels, Reza Mohammadi, Marit Schoonhoven, Ş. İlker Birbil
Gaussian graphical models provide a powerful framework to reveal the conditional dependency structure between multivariate variables. The process of uncovering the conditional dependency network is...
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Models for Multi-State Survival Data: Rates, Risks, and Pseudo-Values J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-08-30 Ross L. Prentice
Published in Journal of the American Statistical Association (Just accepted, 2024)
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Optimal Network Pairwise Comparison J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-08-28 Jiashun Jin, Zheng Tracy Ke, Shengming Luo, Yucong Ma
We are interested in the problem of two-sample network hypothesis testing: given two networks with the same set of nodes, we wish to test whether the underlying Bernoulli probability matrices of th...
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Monte Carlo inference for semiparametric Bayesian regression J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-08-28 Daniel R. Kowal, Bohan Wu
Data transformations are essential for broad applicability of parametric regression models. However, for Bayesian analysis, joint inference of the transformation and model parameters typically invo...
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Robust regression with covariate filtering: Heavy tails and adversarial contamination J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-08-22 Ankit Pensia, Varun Jog, Po-Ling Loh
We study the problem of linear regression where both covariates and responses are potentially (i) heavy-tailed and (ii) adversarially contaminated. Several computationally efficient estimators have...
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Semi-supervised Triply Robust Inductive Transfer Learning J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-08-22 Tianxi Cai, Mengyan Li, Molei Liu
In this work, we propose a Semi-supervised Triply Robust Inductive transFer LEarning (STRIFLE) approach, which integrates heterogeneous data from a label-rich source population and a label-scarce t...
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Natural Gradient Variational Bayes without Fisher Matrix Analytic Calculation and Its Inversion J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-08-22 A. Godichon-Baggioni, D. Nguyen, M-N. Tran
This paper introduces a method for efficiently approximating the inverse of the Fisher information matrix, a crucial step in achieving effective variational Bayes inference. A notable aspect of our...
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Statistical Inference for Networks of High-Dimensional Point Processes J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-08-22 Xu Wang, Mladen Kolar, Ali Shojaie
Fueled in part by recent applications in neuroscience, the multivariate Hawkes process has become a popular tool for modeling the network of interactions among high-dimensional point process data. ...
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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 : 2024-08-22 Reza Mohammadi
Published in Journal of the American Statistical Association (Vol. 119, No. 547, 2024)
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Efficient Multiple Change Point Detection and Localization For High-dimensional Quantile Regression with Heteroscedasticity J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-08-19 Xianru Wang, Bin Liu, Xinsheng Zhang, Yufeng Liu
Data heterogeneity is a challenging issue for modern statistical data analysis. There are different types of data heterogeneity in practice. In this paper, we consider potential structural changes ...
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Functional Integrative Bayesian Analysis of High-dimensional Multiplatform Clinicogenomic Data J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-08-14 Rupam Bhattacharyya, Nicholas C. Henderson, Veerabhadran Baladandayuthapani
Rapid advancements in collection and dissemination of multi-platform molecular and genomics data has resulted in enormous opportunities to aggregate such data in order to understand, prevent, and t...
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Parallel sampling of decomposable graphs using Markov chains on junction trees J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-08-14 Mohamad Elmasri
Bayesian inference for undirected graphical models is mostly restricted to the class of decomposable graphs, as they enjoy a rich set of properties making them amenable to high-dimensional problems...
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Optimal Network Membership Estimation under Severe Degree Heterogeneity J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-08-13 Zheng Tracy Ke, Jingming Wang
Real networks often have severe degree heterogeneity, with maximum, average, and minimum node degrees differing significantly. This paper examines the impact of degree heterogeneity on statistical ...
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Statistical Inference of Cell-type Proportions Estimated from Bulk Expression Data J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-07-23 Biao Cai, Emma Jingfei Zhang, Hongyu Li, Chang Su, Hongyu Zhao
There is a growing interest in cell-type-specific analysis from bulk samples with a mixture of different cell types. A critical first step in such analyses is the accurate estimation of cell-type p...
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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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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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Statistical Methods in Health Disparity Research J. Am. Stat. Assoc. (IF 3.0) Pub Date : 2024-05-20 Susan M. Paddock
Published in Journal of the American Statistical Association (Vol. 119, No. 547, 2024)
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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...