Journal of Multivariate Analysis最新文献_第3页

A method for sparse and robust independent component analysis 稀疏鲁棒独立分量分析方法

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2026-05-01 Epub Date: 2025-12-10 DOI: 10.1016/j.jmva.2025.105587

Lauri Heinonen, Joni Virta

引用次数: 0

Subgroup effect quantile regression with high dimensional missing panel data 高维缺失面板数据的亚组效应分位数回归

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2026-05-01 Epub Date: 2025-12-23 DOI: 10.1016/j.jmva.2025.105593

Shu-Yu Li, Han-Ying Liang

引用次数: 0

Model-free feature screening for ultrahigh dimensional data with responses missing not at random 响应非随机缺失的超高维数据的无模型特征筛选

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2026-05-01 Epub Date: 2026-01-15 DOI: 10.1016/j.jmva.2026.105605

Yuliang Bai, Niansheng Tang

{"title":"Model-free feature screening for ultrahigh dimensional data with responses missing not at random","authors":"Yuliang Bai, Niansheng Tang","doi":"10.1016/j.jmva.2026.105605","DOIUrl":"10.1016/j.jmva.2026.105605","url":null,"abstract":"<div><div>Feature screening method is an important tool for screening active features in ultrahigh dimensional data analysis. Existing feature screening methods mainly focus on the fully observed data or missing responses at random. But in many applied fields such as biomedicine, social science and epidemiological studies, responses might be subject to nonignorable missingness due to various reasons such as dropout. To this end, this paper proposes a new adjusted Spearman rank correlation to screen active features by incorporating the Spearman rank correlation and its conditional expectation in the presence of nonignorable missing responses. To circumvent the notorious identification problem, we introduce instrumental variables into the propensity score (PS) function, which is specified by a more general semiparametric regression model. A nonparametric imputation method is developed to estimate the adjusted Spearman rank correlation. The proposed method has several desirable merits. First, it is model-free. Second, it is robust to outliers, heavy tailed data and the misspecification of the PS function. Third, under some weaker regularity conditions than existing missing data literature, it has sure screening property and ranking consistency, and can well control the false discovery rate regardless of known or consistently estimated parameters in the PS function. Simulation studies and two real examples are used to investigate the performance of the proposed methodologies.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"213 ","pages":"Article 105605"},"PeriodicalIF":1.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977542","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

Nonparametric estimation from correlated copies of a drifted process 漂移过程相关副本的非参数估计

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2026-05-01 Epub Date: 2026-01-17 DOI: 10.1016/j.jmva.2026.105607

Nicolas Marie

引用次数: 0

Multiplier and empirical subsample bootstraps for maxima in high dimensional time series analysis 高维时间序列分析中最大值的乘子和经验子样本自举

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2026-05-01 Epub Date: 2025-12-16 DOI: 10.1016/j.jmva.2025.105579

Ruru Ma, Shibin Zhang

引用次数: 0

Envelope-based partial least squares in functional regression 函数回归中基于包络的偏最小二乘

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2026-05-01 Epub Date: 2025-12-23 DOI: 10.1016/j.jmva.2025.105592

Minxuan Wu , Joseph Antonelli , Zhihua Su

{"title":"Envelope-based partial least squares in functional regression","authors":"Minxuan Wu , Joseph Antonelli , Zhihua Su","doi":"10.1016/j.jmva.2025.105592","DOIUrl":"10.1016/j.jmva.2025.105592","url":null,"abstract":"<div><div>In this article, we extend predictor envelope models to settings with multivariate outcomes and multiple, functional predictors. We propose a two-step estimation strategy, which first projects the function onto a finite-dimensional Euclidean space before fitting the model using existing approaches to envelope models. We first develop an estimator under a linear model with continuous outcomes and then extend this procedure to the more general class of generalized linear models, which allow for a variety of outcome types. We provide asymptotic theory for these estimators showing that they are root-<span><math><mi>n</mi></math></span> consistent and asymptotically normal when the regression coefficient is finite-rank. Additionally we show that consistency can be obtained even when the regression coefficient has rank that grows with the sample size. Extensive simulation studies confirm our theoretical results and show strong prediction performance of the proposed estimators. Additionally, we provide multiple data analyses showing that the proposed approach performs well in real-world settings under a variety of outcome types compared with existing dimension reduction approaches.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"213 ","pages":"Article 105592"},"PeriodicalIF":1.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145837543","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

Simultaneous heterogeneity and reduced-rank learning for multivariate response regression 多元反应回归的同时异质性与降阶学习

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2026-05-01 Epub Date: 2025-12-05 DOI: 10.1016/j.jmva.2025.105578

Jie Wu , Bo Zhang , Daoji Li , Zemin Zheng

{"title":"Simultaneous heterogeneity and reduced-rank learning for multivariate response regression","authors":"Jie Wu , Bo Zhang , Daoji Li , Zemin Zheng","doi":"10.1016/j.jmva.2025.105578","DOIUrl":"10.1016/j.jmva.2025.105578","url":null,"abstract":"<div><div>Heterogeneous data are now ubiquitous in many applications in which correctly identifying the subgroups from a heterogeneous population is critical. Although there is an increasing body of literature on subgroup detection, existing methods mainly focus on the univariate response setting. In this paper, we propose a joint heterogeneity and reduced-rank learning framework to simultaneously identify the subgroup structure and estimate the covariate effects for heterogeneous multivariate response regression. In particular, our approach uses rank-constrained pairwise fusion penalization and conducts the subgroup analysis without requiring prior knowledge regarding the individual subgroup memberships. We implement the proposed approach by an alternating direction method of multipliers (ADMM) algorithm and show its convergence. We also establish the asymptotic properties for the resulting estimators under mild and interpretable conditions. A predictive information criterion is proposed to select the rank of the coefficient matrix with theoretical support. The effectiveness of the proposed approach is demonstrated through simulation studies and a real data application.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"213 ","pages":"Article 105578"},"PeriodicalIF":1.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145683762","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

Robust factor analysis with exponential squared loss 具有指数平方损失的稳健因子分析

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2026-05-01 Epub Date: 2025-12-01 DOI: 10.1016/j.jmva.2025.105567

Jiaqi Hu, Tingyin Wang, Xueqin Wang

{"title":"Robust factor analysis with exponential squared loss","authors":"Jiaqi Hu, Tingyin Wang, Xueqin Wang","doi":"10.1016/j.jmva.2025.105567","DOIUrl":"10.1016/j.jmva.2025.105567","url":null,"abstract":"<div><div>The large dimensional factor model, aimed at reducing dimensionality and extracting features through a few latent common factors, has sparked significant interest due to its broad applications. Despite the popularity of traditional methods for factor models, they may yield incorrect estimators for heavy-tailed data. To address this issue, we introduce the exponential squared loss to the factor model in this study, denoted as the Robust Exponential Factor Analysis (REFA). We propose a modified rank minimization technique to enhance the estimation accuracy of factor numbers in finite-sample cases. Consistency properties for factors and loadings are established under mild conditions, without any moment assumptions on the errors. The performance of REFA with finite samples under both light and heavy-tailed cases has been demonstrated through simulation studies. Furthermore, an analysis employing a financial dataset of asset returns underscores the superiority of REFA. To facilitate the implementation of our proposed methodology by researchers, we have developed an <span>R</span> package named <span>REFA</span>, which is available on <span>CRAN</span>.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"213 ","pages":"Article 105567"},"PeriodicalIF":1.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145683763","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

Low dimensional factor model-based tests for assessing vector correlation in high-dimensional settings 用于评估高维设置中向量相关性的基于低维因素模型的测试

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2026-05-01 Epub Date: 2025-12-17 DOI: 10.1016/j.jmva.2025.105588

Masashi Hyodo , Takahiro Nishiyama , Shoichi Narita

引用次数: 0

A sparse dimension-reduced subspace-based approach for detecting multiple change points in high-dimensional data 一种基于稀疏降维子空间的高维数据多变化点检测方法

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2026-05-01 Epub Date: 2025-12-27 DOI: 10.1016/j.jmva.2025.105594

Luoyao Yu , Rongzhu Zhao , Jiaqi Huang , Lixing Zhu , Xuehu Zhu

引用次数: 0