Journal of Multivariate Analysis最新文献

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 : 2025-09-24 DOI: 10.1016/j.jmva.2025.105488

Jiajia Jiang , Kuangnan Fang , Shuangge Ma , Qingzhao Zhang

引用次数: 0

A novel martingale difference correlation via data splitting with applications in feature screening 一种新的基于数据分割的鞅差相关性及其在特征筛选中的应用

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2025-09-19 DOI: 10.1016/j.jmva.2025.105508

Zhengyu Zhu , Jicai Liu , Riquan Zhang

{"title":"A novel martingale difference correlation via data splitting with applications in feature screening","authors":"Zhengyu Zhu , Jicai Liu , Riquan Zhang","doi":"10.1016/j.jmva.2025.105508","DOIUrl":"10.1016/j.jmva.2025.105508","url":null,"abstract":"<div><div>In this paper, we introduce a novel sample martingale difference correlation via data splitting to measure the departure of conditional mean independence between a response variable <span><math><mi>Y</mi></math></span> and a vector predictor <span><math><mi>X</mi></math></span>. The proposed correlation converges to zero and has an asymptotically symmetric sampling distribution around zero when <span><math><mi>Y</mi></math></span> and <span><math><mi>X</mi></math></span> are conditionally mean independent. In contrast, it converges to a positive value when <span><math><mi>Y</mi></math></span> and <span><math><mi>X</mi></math></span> are conditionally mean dependent. Leveraging these properties, we develop a new model-free feature screening method with false discovery rate (FDR) control for ultrahigh-dimensional data. We demonstrate that this screening method achieves FDR control and the sure screening property simultaneously. We also extend our approach to conditional quantile screening with FDR control. To further enhance the stability of the screening results, we implement multiple splitting techniques. We evaluate the finite sample performance of our proposed methods through simulations and real data analyses, and compare them with existing methods.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"211 ","pages":"Article 105508"},"PeriodicalIF":1.4,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096561","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 nonparametric functional data regression with incomplete observations 不完全观测值下的非参数函数数据回归

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2025-09-17 DOI: 10.1016/j.jmva.2025.105497

Majid Mojirsheibani

{"title":"On nonparametric functional data regression with incomplete observations","authors":"Majid Mojirsheibani","doi":"10.1016/j.jmva.2025.105497","DOIUrl":"10.1016/j.jmva.2025.105497","url":null,"abstract":"<div><div>In this work we consider the problem of nonparametric estimation of a regression function <span><math><mrow><mi>m</mi><mrow><mo>(</mo><mi>χ</mi><mo>)</mo></mrow><mo>=</mo><mi>E</mi><mrow><mo>(</mo><mi>Y</mi><mo>|</mo><mspace></mspace><mi>χ</mi><mo>=</mo><mi>χ</mi><mo>)</mo></mrow></mrow></math></span> with the functional covariate <span><math><mrow><mi>χ</mi></mrow></math></span> when the response <span><math><mi>Y</mi></math></span> may be missing according to a missing-not-at-random (MNAR) setup, i.e., when the underlying missing probability mechanism can depend on both <span><math><mrow><mi>χ</mi></mrow></math></span> and <span><math><mi>Y</mi></math></span>. Our proposed estimator is based on a particular representation of the regression function <span><math><mrow><mi>m</mi><mrow><mo>(</mo><mi>χ</mi><mo>)</mo></mrow></mrow></math></span> in terms of four associated conditional expectations that can be estimated nonparametrically. To assess the theoretical performance of our estimators, we study their convergence properties in general <span><math><msup><mrow><mi>L</mi></mrow><mrow><mi>p</mi></mrow></msup></math></span> norms where we also look into their rates of convergence. Our numerical results show that the proposed estimators have good finite-sample performance. We also explore the applications of our results to the problem of statistical classification with missing labels and establish a number of convergence results for new kernel-type classification rules.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"211 ","pages":"Article 105497"},"PeriodicalIF":1.4,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096559","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

Efficiency of Markov chains for Bayesian linear regression models with heavy-tailed errors 具有重尾误差的贝叶斯线性回归模型的马尔可夫链效率

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2025-09-15 DOI: 10.1016/j.jmva.2025.105506

Yasuyuki Hamura

引用次数: 0

Tree Pólya Splitting distributions for multivariate count data 树Pólya分裂分布的多变量计数数据

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2025-09-12 DOI: 10.1016/j.jmva.2025.105507

Samuel Valiquette , Jean Peyhardi , Éric Marchand , Gwladys Toulemonde , Frédéric Mortier

{"title":"Tree Pólya Splitting distributions for multivariate count data","authors":"Samuel Valiquette , Jean Peyhardi , Éric Marchand , Gwladys Toulemonde , Frédéric Mortier","doi":"10.1016/j.jmva.2025.105507","DOIUrl":"10.1016/j.jmva.2025.105507","url":null,"abstract":"<div><div>In this article, we develop a new class of multivariate distributions adapted for count data, called Tree Pólya Splitting. This class results from the combination of a univariate distribution and singular multivariate distributions along a fixed partition tree. Known distributions, including the Dirichlet-multinomial, the generalized Dirichlet-multinomial and the Dirichlet-tree multinomial, are particular cases within this class. As we will demonstrate, these distributions offer some flexibility, allowing for the modeling of complex dependence structures (positive, negative, or null) at the observation level. Specifically, we present theoretical properties of Tree Pólya Splitting distributions by focusing primarily on marginal distributions, factorial moments, and dependence structures (covariance and correlations). A dataset of abundance of Trichoptera is used, on one hand, as a benchmark to illustrate the theoretical properties developed in this article, and on the other hand, to demonstrate the interest of these types of models, notably by comparing them to other approaches for fitting multivariate data, such as the Poisson-lognormal model in ecology or singular multivariate distributions used in microbial analysis.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"211 ","pages":"Article 105507"},"PeriodicalIF":1.4,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096560","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

A unified selection consistency theorem for information criterion-based rank estimators in factor analysis 因子分析中基于信息准则的秩估计的统一选择一致性定理

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2025-09-03 DOI: 10.1016/j.jmva.2025.105498

Toshinari Morimoto , Hung Hung , Su-Yun Huang

引用次数: 0

Robust semi-functional censored regression 鲁棒半功能删节回归

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2025-08-31 DOI: 10.1016/j.jmva.2025.105491

Tao Wang

{"title":"Robust semi-functional censored regression","authors":"Tao Wang","doi":"10.1016/j.jmva.2025.105491","DOIUrl":"10.1016/j.jmva.2025.105491","url":null,"abstract":"<div><div>This paper develops a robust methodological framework for analyzing randomly censored responses within the semi-functional partial linear regression models, utilizing the exponential squared loss criterion. The proposed methodology capitalizes on the robustness of the exponential squared loss function against outliers and heavy-tailed error distributions, while preserving the flexibility and interpretability of semi-functional regression, which accommodates scalar and functional predictors in a unified framework. To account for the divergent convergence rates of the parametric and nonparametric components, we introduce a novel three-step estimation procedure designed to enhance computational efficiency, ensure model robustness, and achieve asymptotically optimal estimation performance. The parametric component is estimated through a quasi-Newton algorithm, for which we establish global convergence under standard regularity conditions using a Wolfe-type line search strategy. Additionally, we suggest a cross-validation criterion based on the exponential squared loss function to guide the data-driven selection of tuning parameters. The theoretical properties, including consistency and asymptotic normality of the proposed estimators, are established under mild conditions. The efficacy and robustness of the method are demonstrated through a series of simulation studies and an empirical application to Alzheimer’s disease progression, highlighting its practical applicability in addressing complex and censored data structures.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"211 ","pages":"Article 105491"},"PeriodicalIF":1.4,"publicationDate":"2025-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145027204","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 : 2025-08-29 DOI: 10.1016/j.jmva.2025.105496

Xavier Emery , Moreno Bevilacqua , Emilio Porcu

引用次数: 0

Classifying elliptically distributed observations using the Ledoit–Wolf shrinkage approach 使用Ledoit-Wolf收缩方法对椭圆分布的观测进行分类

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2025-08-27 DOI: 10.1016/j.jmva.2025.105495

Rasoul Lotfi , Davood Shahsavani , Mohammad Arashi

引用次数: 0

Posterior contraction and uncertainty quantification for the multivariate spike-and-slab LASSO 多变量刺板LASSO的后验收缩和不确定性量化

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2025-08-18 DOI: 10.1016/j.jmva.2025.105493

Yunyi Shen , Sameer K. Deshpande

{"title":"Posterior contraction and uncertainty quantification for the multivariate spike-and-slab LASSO","authors":"Yunyi Shen , Sameer K. Deshpande","doi":"10.1016/j.jmva.2025.105493","DOIUrl":"10.1016/j.jmva.2025.105493","url":null,"abstract":"<div><div>We study the asymptotic properties of Deshpande et al. (2019)’s multivariate spike-and-slab LASSO (mSSL) procedure for simultaneous variable and covariance selection in the sparse multivariate linear regression problem. In that problem, <span><math><mi>q</mi></math></span> correlated responses are regressed onto <span><math><mi>p</mi></math></span> covariates and the mSSL works by placing separate spike-and-slab priors on the entries in the matrix of marginal covariate effects and off-diagonal elements in the upper triangle of the residual precision matrix. Under mild assumptions about these matrices, we establish the posterior contraction rate for the mSSL posterior in the asymptotic regime where both <span><math><mi>p</mi></math></span> and <span><math><mi>q</mi></math></span> diverge with <span><math><mrow><mi>n</mi><mo>.</mo></mrow></math></span> By “de-biasing” the corresponding MAP estimates, we obtain confidence intervals for each covariate effect and residual partial correlation. In extensive simulation studies, these intervals displayed close-to-nominal frequentist coverage in finite sample settings but tended to be substantially longer than those obtained using a version of the Bayesian bootstrap that randomly re-weights the prior. We further show that the de-biased intervals for individual covariate effects are asymptotically valid.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"210 ","pages":"Article 105493"},"PeriodicalIF":1.4,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144866127","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