发布求助
文献互助 智能选刊 最新文献

Journal of Multivariate Analysis最新文献

筛选
英文 中文
Dimension reduction for outlier detection in high-dimensional data 高维数据中异常点检测的降维方法
IF 1.4 3区 数学
Journal of Multivariate Analysis Pub Date : 2026-01-01 Epub Date: 2025-11-11 DOI: 10.1016/j.jmva.2025.105531
Santiago Ortiz , Henry Laniado , Daniel Peña , Francisco J. Prieto
{"title":"Dimension reduction for outlier detection in high-dimensional data","authors":"Santiago Ortiz ,&nbsp;Henry Laniado ,&nbsp;Daniel Peña ,&nbsp;Francisco J. Prieto","doi":"10.1016/j.jmva.2025.105531","DOIUrl":"10.1016/j.jmva.2025.105531","url":null,"abstract":"<div><div>The work introduces the KASP (Kurtosis and Skewness Projections) procedure, a method for detecting outliers in high-dimensional multivariate data based on dimension reduction techniques. The procedure involves finding projections that maximize non-normality measures in the distribution of the observations. These projections are based on three directions: one that maximizes a combination of the squared skewness and kurtosis coefficients, one that minimizes the kurtosis coefficient, and one that maximizes the squared skewness coefficient. The study demonstrates that these directions include the optimal way to identify outliers for many different contamination structures. The performance of the KASP procedure is compared with alternative methods in correctly identifying and falsely detecting outliers in high-dimensional data sets. Additionally, the paper presents three practical examples to illustrate the effectiveness of the procedure in outlier detection in high dimensions.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"211 ","pages":"Article 105531"},"PeriodicalIF":1.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145516869","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
Dimension selection in tensor decompositions and envelope models 张量分解和包络模型中的维数选择
IF 1.4 3区 数学
Journal of Multivariate Analysis Pub Date : 2026-01-01 Epub Date: 2025-09-25 DOI: 10.1016/j.jmva.2025.105512
Xin Zhang , Wenbiao Zhao , Lixing Zhu
{"title":"Dimension selection in tensor decompositions and envelope models","authors":"Xin Zhang ,&nbsp;Wenbiao Zhao ,&nbsp;Lixing Zhu","doi":"10.1016/j.jmva.2025.105512","DOIUrl":"10.1016/j.jmva.2025.105512","url":null,"abstract":"<div><div>The statistical analysis of tensor-valued data has emerged as an area of increasing methodological focus. Tensor decomposition models and low-rank tensor regression models often assume that there exists a low-rank structure of the tensor data or tensor coefficient. Consistent selection of structural dimensions or tensor ranks constitutes a problem of significant theoretical and practical importance. This paper introduces a unified framework for addressing this challenge, applicable across multiple tensor decomposition frameworks and envelope regression models.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"211 ","pages":"Article 105512"},"PeriodicalIF":1.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145321071","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
Matérn and Generalized Wendland correlation models that parameterize hole effect, smoothness, and support 参数化空穴效应、平滑度和支持度的mat<s:1>和广义温德兰相关模型
IF 1.4 3区 数学
Journal of Multivariate Analysis Pub Date : 2026-01-01 Epub Date: 2025-08-29 DOI: 10.1016/j.jmva.2025.105496
Xavier Emery , Moreno Bevilacqua , Emilio Porcu
{"title":"Matérn and Generalized Wendland correlation models that parameterize hole effect, smoothness, and support","authors":"Xavier Emery ,&nbsp;Moreno Bevilacqua ,&nbsp;Emilio Porcu","doi":"10.1016/j.jmva.2025.105496","DOIUrl":"10.1016/j.jmva.2025.105496","url":null,"abstract":"<div><div>A huge literature in statistics and machine learning is devoted to parametric families of correlation functions, where the correlation parameters are used to understand the properties of an associated spatial random process in terms of smoothness and global or compact support. However, most of current parametric correlation functions attain only non-negative values. This work provides two new families of correlation functions that can have some negative values (aka hole effects), along with smoothness, and global or compact support. They generalize the celebrated Matérn and Generalized Wendland models, respectively, which are obtained as special cases. A link between the two new families is also established, showing that a specific reparameterization of the latter includes the former as a special limit case. Their performance in terms of estimation accuracy and goodness of best linear unbiased prediction is illustrated through synthetic and real data.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"211 ","pages":"Article 105496"},"PeriodicalIF":1.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144934095","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
Properties of CoVaR based on tail expansions of copulas 基于copula尾部展开的CoVaR性质
IF 1.4 3区 数学
Journal of Multivariate Analysis Pub Date : 2026-01-01 Epub Date: 2025-09-29 DOI: 10.1016/j.jmva.2025.105510
Xiaoting Li, Harry Joe
{"title":"Properties of CoVaR based on tail expansions of copulas","authors":"Xiaoting Li,&nbsp;Harry Joe","doi":"10.1016/j.jmva.2025.105510","DOIUrl":"10.1016/j.jmva.2025.105510","url":null,"abstract":"<div><div>The theoretical properties of two widely used CoVaR definitions are investigated under different dependence structures in joint distributions. By using copulas, the dependence is separated from marginal distributions, and CoVaR is expressed through an adjustment factor based solely on the copula. The primary contribution is to study the limiting behavior of the adjustment factor and its link to the strength of dependence in the tails of the joint distribution. We also provide asymptotic results for bivariate Archimedean copulas and extend these findings to extreme value copulas and their mixtures. These findings enhance the understanding of CoVaR in risk scenarios, particularly as the conditional event becomes more extreme.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"211 ","pages":"Article 105510"},"PeriodicalIF":1.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145266692","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 variable selection criteria for the penalized regression 惩罚回归的稳健变量选择标准
IF 1.4 3区 数学
Journal of Multivariate Analysis Pub Date : 2026-01-01 Epub Date: 2025-11-14 DOI: 10.1016/j.jmva.2025.105540
Abhijit Mandal , Samiran Ghosh
{"title":"Robust variable selection criteria for the penalized regression","authors":"Abhijit Mandal ,&nbsp;Samiran Ghosh","doi":"10.1016/j.jmva.2025.105540","DOIUrl":"10.1016/j.jmva.2025.105540","url":null,"abstract":"<div><div>We develop a robust variable selection framework that integrates divergence-based M-estimation with penalization. The proposed method yields regression parameter estimates that are resistant to outliers while simultaneously identifying the most relevant explanatory variables. The asymptotic distribution and influence function of the estimators are derived. Classical model selection criteria such as Mallows’ <span><math><msub><mrow><mi>C</mi></mrow><mrow><mi>p</mi></mrow></msub></math></span> and the Akaike information criterion (AIC) are known to deteriorate under heavy-tailed errors or contamination. To address this issue, we introduce robust counterparts of these criteria, constructed from our divergence-based estimators. The proposed approach substantially improves variable selection and prediction performance in the presence of outliers, while maintaining competitiveness with state-of-the-art robust high-dimensional methods. The practical utility of the procedure is further demonstrated through an analysis of the plasma Beta-Carotene dataset.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"211 ","pages":"Article 105540"},"PeriodicalIF":1.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145516865","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
Unsupervised linear discrimination using skewness 使用偏度的无监督线性判别
IF 1.4 3区 数学
Journal of Multivariate Analysis Pub Date : 2026-01-01 Epub Date: 2025-11-08 DOI: 10.1016/j.jmva.2025.105524
Una Radojičić , Klaus Nordhausen , Joni Virta
{"title":"Unsupervised linear discrimination using skewness","authors":"Una Radojičić ,&nbsp;Klaus Nordhausen ,&nbsp;Joni Virta","doi":"10.1016/j.jmva.2025.105524","DOIUrl":"10.1016/j.jmva.2025.105524","url":null,"abstract":"<div><div>It is well-known that, in Gaussian two-group separation, the optimally discriminating projection direction can be estimated without any knowledge on the group labels. In this work, we gather several such unsupervised estimators based on skewness and derive their limiting distributions. As one of our main results, we show that all affine equivariant estimators of the optimal direction have proportional asymptotic covariance matrices, making their comparison straightforward. Two of our four estimators are novel and two have been proposed already earlier. We use simulations to verify our results and to inspect the finite-sample behaviors of the estimators.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"211 ","pages":"Article 105524"},"PeriodicalIF":1.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145516976","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
Model-based Fréchet regression in (quotient) metric spaces with a focus on elastic curves （商）度量空间中基于模型的frsamchet回归，重点是弹性曲线
IF 1.4 3区 数学
Journal of Multivariate Analysis Pub Date : 2026-01-01 Epub Date: 2025-10-08 DOI: 10.1016/j.jmva.2025.105515
Lisa Steyer , Almond Stöcker , Sonja Greven , Alzheimer’s Disease Neuroimaging Initiative
{"title":"Model-based Fréchet regression in (quotient) metric spaces with a focus on elastic curves","authors":"Lisa Steyer ,&nbsp;Almond Stöcker ,&nbsp;Sonja Greven ,&nbsp;Alzheimer’s Disease Neuroimaging Initiative","doi":"10.1016/j.jmva.2025.105515","DOIUrl":"10.1016/j.jmva.2025.105515","url":null,"abstract":"<div><div>We introduce model-based Fréchet regression in metric spaces. Instead of starting from point-wise conditional Fréchet means, our approach is defined as a constrained minimization problem over a model class of functions. The approach is then applied to develop a general framework of regression for quotient metric spaces with distances induced by isometric group actions. Such spaces arise naturally in applications where objects are considered equivalent up to transformations. We first establish general existence and consistency results for model-based Fréchet regression, with our quotient space regression model as a special case. As an important example we consider regression for elastic curves in the square-root velocity framework. This addresses data such as handwritten letters, movement paths, or outlines of objects, where only the image but not the parametrization of the curves is of interest. To handle sparsely or irregularly sampled curves, we model smooth conditional mean curves using splines. We validate our approach through simulations and an application to hippocampal outlines extracted from Magnetic Resonance Imaging scans. Here we model how the shape of the irregularly sampled hippocampus is related to age, Alzheimer’s disease and sex, to disentangle the shrinking effects of Alzheimer’s from normal aging.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"211 ","pages":"Article 105515"},"PeriodicalIF":1.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145321069","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
Hierarchical structure-guided high-dimensional multi-view clustering 层次结构引导的高维多视图聚类
IF 1.4 3区 数学
Journal of Multivariate Analysis Pub Date : 2026-01-01 Epub Date: 2025-09-24 DOI: 10.1016/j.jmva.2025.105488
Jiajia Jiang , Kuangnan Fang , Shuangge Ma , Qingzhao Zhang
{"title":"Hierarchical structure-guided high-dimensional multi-view clustering","authors":"Jiajia Jiang ,&nbsp;Kuangnan Fang ,&nbsp;Shuangge Ma ,&nbsp;Qingzhao Zhang","doi":"10.1016/j.jmva.2025.105488","DOIUrl":"10.1016/j.jmva.2025.105488","url":null,"abstract":"<div><div>Multi-view data clustering is pivotal for comprehending the heterogeneous structure of data by integrating information from diverse aspects. Nevertheless, practical challenges arise due to the differences in the granularity from different views, resulting in a hierarchical clustering structure within these distinct data types. In this work, we consider such structure information and propose a novel high-dimensional multi-view clustering approach with a hierarchical structure across views. The proposed non-convex problem is effectively tackled using the Alternating Direction Method of Multipliers algorithm, and we establish the statistical properties of the estimator. Simulation results demonstrate the effectiveness and superiority of our proposed method. In the analysis of the histopathological imaging data and gene expression data related to lung adenocarcinoma, our method unveils a hierarchical clustering structure that significantly diverges from alternative approaches.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"211 ","pages":"Article 105488"},"PeriodicalIF":1.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145155083","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
Enhancing spatial functional linear regression with robust dimension reduction methods 用稳健降维方法增强空间泛函线性回归
IF 1.4 3区 数学
Journal of Multivariate Analysis Pub Date : 2026-01-01 Epub Date: 2025-11-10 DOI: 10.1016/j.jmva.2025.105538
Ufuk Beyaztas , Abhijit Mandal , Han Lin Shang
{"title":"Enhancing spatial functional linear regression with robust dimension reduction methods","authors":"Ufuk Beyaztas ,&nbsp;Abhijit Mandal ,&nbsp;Han Lin Shang","doi":"10.1016/j.jmva.2025.105538","DOIUrl":"10.1016/j.jmva.2025.105538","url":null,"abstract":"<div><div>This paper introduces a robust estimation strategy for the spatial functional linear regression model using dimension reduction methods, specifically functional principal component analysis (FPCA) and functional partial least squares (FPLS). These techniques are designed to address challenges associated with spatially correlated functional data, particularly the impact of outliers on parameter estimation. By projecting the infinite-dimensional functional predictor onto a finite-dimensional space defined by orthonormal basis functions and employing M-estimation to mitigate outlier effects, our approach improves the accuracy and reliability of parameter estimates in the spatial functional linear regression context. Simulation studies and empirical data analysis substantiate the effectiveness of our methods. Fisher consistency and influence function of the FPCA-based approach are established under regularity conditions. The <span>rfsac</span> package in <figure><img></figure> <span><span><sup>1</sup></span></span> implements these robust estimation strategies, ensuring practical applicability for researchers and practitioners.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"211 ","pages":"Article 105538"},"PeriodicalIF":1.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145516974","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
Ultra-high dimensional semiparametric dynamic high-order spatial autoregressive models 超高维半参数动态高阶空间自回归模型
IF 1.4 3区 数学
Journal of Multivariate Analysis Pub Date : 2026-01-01 Epub Date: 2025-10-30 DOI: 10.1016/j.jmva.2025.105516
Feng Luo , Hongxia Xu , Guoliang Fan , Liping Zhu
{"title":"Ultra-high dimensional semiparametric dynamic high-order spatial autoregressive models","authors":"Feng Luo ,&nbsp;Hongxia Xu ,&nbsp;Guoliang Fan ,&nbsp;Liping Zhu","doi":"10.1016/j.jmva.2025.105516","DOIUrl":"10.1016/j.jmva.2025.105516","url":null,"abstract":"<div><div>Motivated by the need to effectively characterize complex spatial dependencies inherent in ultra-high dimensional data, this paper develops a sparse semiparametric framework for modeling dynamic high-order spatial autoregressive processes. In this framework, the number of covariates in the linear component grows at a rate much faster than the sample size under a sparsity assumption, whereas the nonparametric component remains of fixed dimension. The varying coefficients are approximated using B-spline basis functions. To address the endogeneity arising from spatial lag terms, two-stage sieve least squares together with instrumental variable methods are employed. We investigate the theoretical properties of the oracle estimator, assuming that the true sparsity structure is known, and establish its convergence rates and asymptotic normality. Further, we propose a nonconvex penalized estimation procedure that simultaneously performs variable selection and estimates both the linear and spatial autoregressive parameters, and we show that it possesses the oracle property under mild conditions. The effectiveness of the proposed method is demonstrated through simulation studies and an empirical application to the Communities and Crime data set from the UCI Machine Learning Repository.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"211 ","pages":"Article 105516"},"PeriodicalIF":1.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145416650","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
首页 上一页 下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：604180095
Book学术官方微信
小红书