Journal of Multivariate Analysis最新文献_第4页

Automatic sparse estimation of the high-dimensional cross-covariance matrix 高维交叉协方差矩阵的自动稀疏估计

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2026-05-01 Epub Date: 2025-12-19 DOI: 10.1016/j.jmva.2025.105590

Tetsuya Umino , Kazuyoshi Yata , Makoto Aoshima

{"title":"Automatic sparse estimation of the high-dimensional cross-covariance matrix","authors":"Tetsuya Umino , Kazuyoshi Yata , Makoto Aoshima","doi":"10.1016/j.jmva.2025.105590","DOIUrl":"10.1016/j.jmva.2025.105590","url":null,"abstract":"<div><div>Scenarios involving high-dimensional, low-sample-size (HDLSS) data are often encountered in modern scientific fields involving genetic microarrays, medical imaging, and finance, where the number of variables can greatly exceed the number of observations. In such settings, a reliable estimation of cross-covariance structures is essential for understanding relationships between variable sets. However, classical estimators often exhibit severe noise accumulation. To address this issue, in this study, we propose a novel thresholding estimator of the cross-covariance matrix for HDLSS settings. We consider the asymptotic properties of the sample cross-covariance matrix and show that the estimator contains large amounts of noise in the high-dimensional setting, which renders it inconsistent. To solve this problem occurring in high-dimensional settings, we develop a new thresholding estimator based on the automatic sparse estimation methodology and show that the estimator is consistent under mild assumptions. We analyze and evaluate the performance of the proposed estimator based on numerical simulations and actual data analysis. The simulations demonstrate that the method attains consistency without requiring the stringent high-dimensional conditions assumed by existing approaches, and the real-data analysis illustrates its applicability to high-dimensional regression problems, wherein improved parameter estimation enhances prediction accuracy. In conclusion, our findings serve as a theoretically sound tool for cross-covariance estimation in HDLSS contexts, with potential implications for a wide range of high-dimensional data analyses.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"213 ","pages":"Article 105590"},"PeriodicalIF":1.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145837541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bounds and identification on direct and indirect effects under partially observed mediator-endpoint confounders 在部分观察到的中介-终点混杂因素下直接和间接影响的界限和鉴定

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2026-05-01 Epub Date: 2025-12-03 DOI: 10.1016/j.jmva.2025.105565

Yu Han , Peng Luo , Wei Zhang , Xiang Gu

引用次数: 0

A general framework to extend sufficient dimension reductions to the cases of the mixture multivariate elliptical distributions 给出了将足够降维扩展到混合多元椭圆分布的一般框架

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2026-05-01 Epub Date: 2025-11-28 DOI: 10.1016/j.jmva.2025.105551

Wenjuan Li , Hongming Pei , Ali Jiang , Fei Chen

引用次数: 0

Simultaneous variable selection and estimation of multivariate panel count data 多变量面板计数数据的同时变量选择与估计

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2026-05-01 Epub Date: 2025-12-02 DOI: 10.1016/j.jmva.2025.105559

Lei Ge , Rong Liu , Tao Hu , Jianguo Sun

引用次数: 0

The mean tests with high dimensional data 高维数据的均值检验

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2026-03-01 Epub Date: 2025-11-28 DOI: 10.1016/j.jmva.2025.105564

Wenzhi Yang , Chi Yao , Yiming Liu , Guangming Pan , Wang Zhou

引用次数: 0

Iterative sequential screening strategies for sparse recovery with computational advantages 具有计算优势的稀疏恢复迭代顺序筛选策略

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2026-03-01 Epub Date: 2025-11-29 DOI: 10.1016/j.jmva.2025.105570

Weixiong Liang , Yuehan Yang

{"title":"Iterative sequential screening strategies for sparse recovery with computational advantages","authors":"Weixiong Liang , Yuehan Yang","doi":"10.1016/j.jmva.2025.105570","DOIUrl":"10.1016/j.jmva.2025.105570","url":null,"abstract":"<div><div>A prominent problem in multi-response models is the presence of complex group structures of the high-dimensional data such as the overlapping group structures. In such models, both responses and predictors are grouped, and each response group is allowed to relate to multiple predictor groups. Ignoring such structures often yields insufficient statistical inference and misleading statistical conclusions. Motivated by practical needs and the sequential canonical correlation search (SCCS) algorithm proposed by Luo and Chen (2020), this paper proposes two computationally attractive feature selection algorithms, reallocating-SCCS (RSCCS) and prescreening-SCCS (PSCCS), for the high-dimensional multi-response models with complex group structures.</div><div>The proposed methods, RSCCS and PSCCS, consist of three steps. In the first step, to fully incorporate the information of group structures in the feature selection algorithm, both RSCCS and PSCCS select a non-zero coefficient block according to the canonical correlation between the residual response groups and feature groups. In the second step, RSCCS selects the non-zero coefficient row, while PSCCS conducts screening within the non-zero coefficient block using penalized regularizations. In the third step, RSCCS and PSCCS select features by EBIC based on different situations and different iterations. We demonstrate the advantages of these two methods compared with several existing approaches. The statistical guarantees of RSCCS and PSCCS are established. We provide numerical simulation results and analyze a real data example to compare their performance with other methods.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"212 ","pages":"Article 105570"},"PeriodicalIF":1.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145616474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On the use of the Gram matrix for multivariate functional principal components analysis 格拉姆矩阵在多元泛函主成分分析中的应用

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2026-03-01 Epub Date: 2025-11-13 DOI: 10.1016/j.jmva.2025.105525

Steven Golovkine , Edward Gunning , Andrew J. Simpkin , Norma Bargary

引用次数: 0

Rank-based combination independence tests for high-dimensional data 高维数据的基于秩的组合独立性检验

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2026-03-01 Epub Date: 2025-11-28 DOI: 10.1016/j.jmva.2025.105550

Liqi Xia , Ruiyuan Cao , Jiang Du , Ling Liu

{"title":"Rank-based combination independence tests for high-dimensional data","authors":"Liqi Xia , Ruiyuan Cao , Jiang Du , Ling Liu","doi":"10.1016/j.jmva.2025.105550","DOIUrl":"10.1016/j.jmva.2025.105550","url":null,"abstract":"<div><div>This paper proposes a novel approach to enhance the versatility of the max-sum test in high-dimensional data analysis by combining two distinct rank correlation coefficients: Spearman’s <span><math><mi>ρ</mi></math></span> and Chatterjee’s <span><math><mi>ξ</mi></math></span>. We uncovered the independence between the max-type test and the sum-type test by deriving their joint distribution. This insight enables the development of a comprehensive max-sum test that tackles both sparse and dense alternative correlation structures in an adaptive manner. Leveraging the asymptotic independence between the two coefficients and the intrinsic highlights of two single-coefficient tests, we have strategically implemented Cauchy combination principles to devise a multifunctional testing methodology. This approach can accommodate monotonic and nonmonotonic data types and thus offers a versatile solution to a broad spectrum of analytical requirements. This versatility of our proposed method has been impressively demonstrated through a diverse range of simulation data studies and two real-world data analyses, underscoring its effectiveness and practical utility.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"212 ","pages":"Article 105550"},"PeriodicalIF":1.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145681889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Variable selection in mixture regression for longitudinal data based on joint mean–covariance model 基于联合均值-协方差模型的纵向数据混合回归变量选择

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2026-03-01 Epub Date: 2025-11-19 DOI: 10.1016/j.jmva.2025.105548

Jing Yu , Jianxin Pan

{"title":"Variable selection in mixture regression for longitudinal data based on joint mean–covariance model","authors":"Jing Yu , Jianxin Pan","doi":"10.1016/j.jmva.2025.105548","DOIUrl":"10.1016/j.jmva.2025.105548","url":null,"abstract":"<div><div>A large number of explanatory variables may be measured with the collection of longitudinal data, of which some may not be influential for modeling of heterogeneous longitudinal data. For such complex data, not only their mean but also covariances may be affected by various explanatory variables. A data-driven approach is proposed to model the mean and covariance structures, simultaneously, together with selecting influential explanatory variables. A penalized maximum likelihood method for the joint mean and covariance model is developed within the framework of finite Gaussian mixture regression. EM algorithm is employed for the numerical calculation. The parameter estimators obtained are shown to be consistent and asymptotically normally distributed, and have oracle properties with proper choices of penalty function and tuning parameter. Simulation studies show that the proposed method works very well and provides accurate and effective parameter estimators by conducting variable selection. For illustration, real data analysis on clustering COVID-19 infected cases for European countries in terms of governmental policy effects is made to demonstrate the usefulness of the proposed method.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"212 ","pages":"Article 105548"},"PeriodicalIF":1.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145571180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Statistical guarantees for distribution estimation of contaminated data via DNN-based MoM-GANs 基于dnn的mom - gan对污染数据分布估计的统计保证

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2026-03-01 Epub Date: 2025-11-28 DOI: 10.1016/j.jmva.2025.105571

Fang Xie , Lihu Xu , Qiuran Yao , Huiming Zhang

{"title":"Statistical guarantees for distribution estimation of contaminated data via DNN-based MoM-GANs","authors":"Fang Xie , Lihu Xu , Qiuran Yao , Huiming Zhang","doi":"10.1016/j.jmva.2025.105571","DOIUrl":"10.1016/j.jmva.2025.105571","url":null,"abstract":"<div><div>This paper investigates the distribution estimation of contaminated data using the MoM-GAN method, which leverages the power of generative adversarial nets (GANs) and median-of-means (MoM) estimation. Specifically, we use a deep neural network (DNN) with a ReLU activation function to model the generator and discriminator of the GAN. In terms of theoretical analysis, we derive a non-asymptotic error bound for the DNN-based MoM-GAN estimator, which is measured by integral probability metrics and takes into account the <span><math><mi>b</mi></math></span>-smoothness Hölder class. The error bound essentially decreases in <span><math><mrow><msup><mrow><mi>n</mi></mrow><mrow><mo>−</mo><mi>b</mi><mo>/</mo><mi>p</mi></mrow></msup><mo>∨</mo><msup><mrow><mi>n</mi></mrow><mrow><mo>−</mo><mn>1</mn><mo>/</mo><mn>2</mn></mrow></msup></mrow></math></span>, where <span><math><mi>n</mi></math></span> and <span><math><mi>p</mi></math></span> are the sample size and the dimension of the input data, respectively. It provides a rigorous guarantee of the accuracy and robustness of the MoM-GAN estimator, even in the presence of contaminated data. We present an algorithm for the MoM-GAN method and demonstrate its effectiveness in two real-world applications. Our results show that the MoM-GAN method outperforms other competing methods when dealing with contaminated data, highlighting its superior performance and robustness.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"212 ","pages":"Article 105571"},"PeriodicalIF":1.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145681883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0