Pub Date : 2026-07-01Epub Date: 2026-02-09DOI: 10.1016/j.jmva.2026.105620
Anh Tuan Bui
Conditional multidimensional scaling seeks for a low-dimensional configuration from pairwise dissimilarities, in the presence of other known features. By taking advantage of available data of the known features, conditional multidimensional scaling improves the estimation quality of the low-dimensional configuration and simplifies knowledge discovery tasks. However, existing conditional multidimensional scaling methods require full data of the known features, which may not be always attainable due to time, cost, and other constraints. This paper proposes a conditional multidimensional scaling method that can learn the low-dimensional configuration when there are missing values in the known features. The method can also impute the missing values, which provides additional insights of the problem. Computer codes of this method are maintained in the cmlR package on CRAN.
{"title":"Conditional multidimensional scaling with incomplete conditioning data","authors":"Anh Tuan Bui","doi":"10.1016/j.jmva.2026.105620","DOIUrl":"10.1016/j.jmva.2026.105620","url":null,"abstract":"<div><div>Conditional multidimensional scaling seeks for a low-dimensional configuration from pairwise dissimilarities, in the presence of other known features. By taking advantage of available data of the known features, conditional multidimensional scaling improves the estimation quality of the low-dimensional configuration and simplifies knowledge discovery tasks. However, existing conditional multidimensional scaling methods require full data of the known features, which may not be always attainable due to time, cost, and other constraints. This paper proposes a conditional multidimensional scaling method that can learn the low-dimensional configuration when there are missing values in the known features. The method can also impute the missing values, which provides additional insights of the problem. Computer codes of this method are maintained in the <strong>cml</strong> <span>R</span> package on CRAN.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"214 ","pages":"Article 105620"},"PeriodicalIF":1.4,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146170930","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}
Pub Date : 2026-07-01Epub Date: 2026-02-05DOI: 10.1016/j.jmva.2026.105616
Matteo Barigozzi , Yong He , Lingxiao Li , Lorenzo Trapani
Tensor Factor Models (TFM) are appealing dimension reduction tools for high-order large-dimensional tensor time series, and have wide applications in economics, finance and medical imaging. In this paper, we propose a projection estimator for the Tucker-decomposition based TFM, and provide its least-square interpretation which parallels to the least-square interpretation of the Principal Component Analysis (PCA) for the vector factor model. The projection technique simultaneously reduces the dimensionality of the signal component and the magnitudes of the idiosyncratic component tensor, thus leading to an increase of the signal-to-noise ratio. We derive a convergence rate of the projection estimator of the loadings and the common factor tensor which are faster than that of the naive PCA-based estimator. Our results are obtained under mild conditions which allow the idiosyncratic components to be weakly cross- and auto- correlated. We also provide a novel iterative procedure based on the eigenvalue-ratio principle to determine the factor numbers. Extensive numerical studies are conducted to investigate the empirical performance of the proposed projection estimators relative to the state-of-the-art ones.
{"title":"Statistical inference for large-dimensional tensor factor model by iterative projections","authors":"Matteo Barigozzi , Yong He , Lingxiao Li , Lorenzo Trapani","doi":"10.1016/j.jmva.2026.105616","DOIUrl":"10.1016/j.jmva.2026.105616","url":null,"abstract":"<div><div>Tensor Factor Models (TFM) are appealing dimension reduction tools for high-order large-dimensional tensor time series, and have wide applications in economics, finance and medical imaging. In this paper, we propose a projection estimator for the Tucker-decomposition based TFM, and provide its least-square interpretation which parallels to the least-square interpretation of the Principal Component Analysis (PCA) for the vector factor model. The projection technique simultaneously reduces the dimensionality of the signal component and the magnitudes of the idiosyncratic component tensor, thus leading to an increase of the signal-to-noise ratio. We derive a convergence rate of the projection estimator of the loadings and the common factor tensor which are faster than that of the naive PCA-based estimator. Our results are obtained under mild conditions which allow the idiosyncratic components to be weakly cross- and auto- correlated. We also provide a novel iterative procedure based on the eigenvalue-ratio principle to determine the factor numbers. Extensive numerical studies are conducted to investigate the empirical performance of the proposed projection estimators relative to the state-of-the-art ones.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"214 ","pages":"Article 105616"},"PeriodicalIF":1.4,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146170931","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}
Pub Date : 2026-07-01Epub Date: 2026-02-03DOI: 10.1016/j.jmva.2026.105618
Annesha Deb , Minerva Mukhopadhyay , Subhajit Dutta
This paper investigates the effectiveness of using the Random Projection Ensemble (RPE) approach in Quadratic Discriminant Analysis (QDA) for ultrahigh-dimensional classification problems. Classical methods such as Linear Discriminant Analysis (LDA) and QDA are used widely, but face significant challenges in their implementation when the data dimension (say, ) exceeds the sample size (say, ). In particular, both LDA (using the Moore–Penrose inverse for covariance matrices) and QDA (even with known covariance matrices) may perform as poorly as unbiased random guessing when as . The RPE method, known for addressing curse of dimensionality, offers a fast and effective solution without relying on selective summary measures of the competing distributions. This paper demonstrates the practical advantages of employing RPE on QDA in terms of classification performance as well as computational efficiency. We establish results for limiting perfect classification in both the population and sample versions of the proposed RPE-QDA classifier, under fairly general assumptions that allow for sub-exponential growth of relative to . Several simulated and real data sets are analyzed to evaluate the performance of the proposed classifier in ultrahigh-dimensional scenario.
{"title":"Ultrahigh-dimensional quadratic discriminant analysis using random projections","authors":"Annesha Deb , Minerva Mukhopadhyay , Subhajit Dutta","doi":"10.1016/j.jmva.2026.105618","DOIUrl":"10.1016/j.jmva.2026.105618","url":null,"abstract":"<div><div>This paper investigates the effectiveness of using the Random Projection Ensemble (RPE) approach in Quadratic Discriminant Analysis (QDA) for ultrahigh-dimensional classification problems. Classical methods such as Linear Discriminant Analysis (LDA) and QDA are used widely, but face significant challenges in their implementation when the data dimension (say, <span><math><mi>p</mi></math></span>) exceeds the sample size (say, <span><math><mi>n</mi></math></span>). In particular, both LDA (using the Moore–Penrose inverse for covariance matrices) and QDA (even with known covariance matrices) may perform as poorly as unbiased random guessing when <span><math><mrow><mi>p</mi><mo>/</mo><mi>n</mi><mo>→</mo><mi>∞</mi></mrow></math></span> as <span><math><mrow><mi>n</mi><mo>→</mo><mi>∞</mi></mrow></math></span>. The RPE method, known for addressing curse of dimensionality, offers a fast and effective solution without relying on selective summary measures of the competing distributions. This paper demonstrates the practical advantages of employing RPE on QDA in terms of classification performance as well as computational efficiency. We establish results for limiting perfect classification in both the population and sample versions of the proposed RPE-QDA classifier, under fairly general assumptions that allow for sub-exponential growth of <span><math><mi>p</mi></math></span> relative to <span><math><mi>n</mi></math></span>. Several simulated and real data sets are analyzed to evaluate the performance of the proposed classifier in ultrahigh-dimensional scenario.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"214 ","pages":"Article 105618"},"PeriodicalIF":1.4,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146171062","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}
Pub Date : 2026-07-01Epub Date: 2026-03-10DOI: 10.1016/j.jmva.2026.105628
Siddhartha Nandy , Chae Young Lim , Tapabrata Maiti
Discerning dependence to prevent potential misinterpretations in statistical contexts involving spatial processes is pivotal for making accurate inferences and predictions. Despite the existing plethora of approaches for spatial covariance matrix estimation, there is a scarcity of viable methods for handling large spatial non-stationary covariance matrices with robust theoretical underpinnings. To bridge this gap, we consider a non-stationary covariance structure by a linear combination of random effects with basis functions that accommodate spatial structures and develop a theoretically valid estimation approach. Our specific interest lies in estimating the number of random effects and their corresponding covariance structure, which is subsequently employed in spatial prediction. We have innovatively leveraged a group LASSO penalized likelihood method to select and estimate rows of the Cholesky factor for the covariance matrix of random effects, with each row penalized as a group. The procedure remains computationally simple. We have probed into the selection consistency and oracle property of the estimated rows of the lower triangular Cholesky factor. Simulation studies support our theoretical findings. Evaluating the prediction performance of our method using real data is also provided in the supplementary materials.
{"title":"Consistent estimation of low-rank spatial covariance matrix: A penalized random effects approach","authors":"Siddhartha Nandy , Chae Young Lim , Tapabrata Maiti","doi":"10.1016/j.jmva.2026.105628","DOIUrl":"10.1016/j.jmva.2026.105628","url":null,"abstract":"<div><div>Discerning dependence to prevent potential misinterpretations in statistical contexts involving spatial processes is pivotal for making accurate inferences and predictions. Despite the existing plethora of approaches for spatial covariance matrix estimation, there is a scarcity of viable methods for handling large spatial non-stationary covariance matrices with robust theoretical underpinnings. To bridge this gap, we consider a non-stationary covariance structure by a linear combination of random effects with basis functions that accommodate spatial structures and develop a theoretically valid estimation approach. Our specific interest lies in estimating the number of random effects and their corresponding covariance structure, which is subsequently employed in spatial prediction. We have innovatively leveraged a group LASSO penalized likelihood method to select and estimate rows of the Cholesky factor for the covariance matrix of random effects, with each row penalized as a group. The procedure remains computationally simple. We have probed into the selection consistency and oracle property of the estimated rows of the lower triangular Cholesky factor. Simulation studies support our theoretical findings. Evaluating the prediction performance of our method using real data is also provided in the supplementary materials.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"214 ","pages":"Article 105628"},"PeriodicalIF":1.4,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147385614","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}
Pub Date : 2026-07-01Epub Date: 2026-02-14DOI: 10.1016/j.jmva.2026.105623
Fang Lu, Kaili Zhu, Jing Yang
This paper considers simultaneous estimation and selection of the spatial autoregressive model with semi-parametric functional coefficients, which not only allows for cross-sectional dependence but also adapts the relationship between predictors and response over different domains of interest, such as time. In contrast to the conventional variable selection procedure focusing on entire region, our proposed method is designed for identification of subregions on which the functional coefficients are zero, to deeply learn the local sparsity of dynamic effects of the significant predictors, as well as to achieve more interpretable estimation at the meantime. Towards this goal, the smoothing spline approximation, regularization mechanism and generalized method of moments estimation approach are incorporated into one framework. Asymptotic properties of the resulting estimator are rigorously established under some regularity conditions, and a practical iterative algorithm is provided for implementation. Abundant simulation studies confirm the theoretical results and superior performance of the proposed method, when compared to other competitors. Three empirical examples are analyzed for practical applications.
{"title":"Simultaneous estimation and domain selection for the spatial autoregressive model with semi-parametric functional coefficients","authors":"Fang Lu, Kaili Zhu, Jing Yang","doi":"10.1016/j.jmva.2026.105623","DOIUrl":"10.1016/j.jmva.2026.105623","url":null,"abstract":"<div><div>This paper considers simultaneous estimation and selection of the spatial autoregressive model with semi-parametric functional coefficients, which not only allows for cross-sectional dependence but also adapts the relationship between predictors and response over different domains of interest, such as time. In contrast to the conventional variable selection procedure focusing on entire region, our proposed method is designed for identification of subregions on which the functional coefficients are zero, to deeply learn the local sparsity of dynamic effects of the significant predictors, as well as to achieve more interpretable estimation at the meantime. Towards this goal, the smoothing spline approximation, regularization mechanism and generalized method of moments estimation approach are incorporated into one framework. Asymptotic properties of the resulting estimator are rigorously established under some regularity conditions, and a practical iterative algorithm is provided for implementation. Abundant simulation studies confirm the theoretical results and superior performance of the proposed method, when compared to other competitors. Three empirical examples are analyzed for practical applications.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"214 ","pages":"Article 105623"},"PeriodicalIF":1.4,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147385532","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}
Pub Date : 2026-07-01Epub Date: 2026-03-06DOI: 10.1016/j.jmva.2026.105632
Olha Bodnar , Taras Bodnar
In the paper, we develop Bayesian inference procedures for the model parameters of the multivariate location-scale model connected to the multivariate Birge ratio method, a novel approach for pooling multivariate measurements together which extends the widely-used univariate Birge ratio method. In particular, the expressions of the joint posterior, the marginal posterior and the conditional posterior distributions are derived. These findings lead to the introduction of the Metropolis–Hastings algorithm and the Gibbs sampler approach for drawing samples from the joint posterior distribution and for conducting Bayesian inference procedures based on the simulated samples. The findings of the paper are implemented in an empirical illustration by studying the effectiveness of the hypertension treatment. It is found that the anti-hypertension drugs lead to the statistically significant reduction of the systolic and diastolic blood pressure as well as to the reduction of the risk of cardiovascular disease and stroke.
{"title":"Bayesian multivariate meta-analysis by using the Birge ratio method","authors":"Olha Bodnar , Taras Bodnar","doi":"10.1016/j.jmva.2026.105632","DOIUrl":"10.1016/j.jmva.2026.105632","url":null,"abstract":"<div><div>In the paper, we develop Bayesian inference procedures for the model parameters of the multivariate location-scale model connected to the multivariate Birge ratio method, a novel approach for pooling multivariate measurements together which extends the widely-used univariate Birge ratio method. In particular, the expressions of the joint posterior, the marginal posterior and the conditional posterior distributions are derived. These findings lead to the introduction of the Metropolis–Hastings algorithm and the Gibbs sampler approach for drawing samples from the joint posterior distribution and for conducting Bayesian inference procedures based on the simulated samples. The findings of the paper are implemented in an empirical illustration by studying the effectiveness of the hypertension treatment. It is found that the anti-hypertension drugs lead to the statistically significant reduction of the systolic and diastolic blood pressure as well as to the reduction of the risk of cardiovascular disease and stroke.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"214 ","pages":"Article 105632"},"PeriodicalIF":1.4,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147385538","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}
Pub Date : 2026-07-01Epub Date: 2026-02-10DOI: 10.1016/j.jmva.2026.105619
Yuanya Xu, Weiming Li
This paper presents a new method for evaluating the significance of correlations in high-dimensional contexts. The test statistic aggregates U-statistic based estimates of pairwise correlations raised to the -th power, preserving invariance under location and scale transformations. We demonstrate that, under the null hypothesis, a collection of such statistics with distinct power parameters converges to a multivariate Gaussian distribution with identity covariance. This theoretical framework facilitates an efficient simultaneous testing procedure that incorporates multiple power parameters.
{"title":"Tests for the significance of a correlation matrix via ℓa-norms in high-dimensions","authors":"Yuanya Xu, Weiming Li","doi":"10.1016/j.jmva.2026.105619","DOIUrl":"10.1016/j.jmva.2026.105619","url":null,"abstract":"<div><div>This paper presents a new method for evaluating the significance of correlations in high-dimensional contexts. The test statistic aggregates U-statistic based estimates of pairwise correlations raised to the <span><math><mi>a</mi></math></span>-th power, preserving invariance under location and scale transformations. We demonstrate that, under the null hypothesis, a collection of such statistics with distinct power parameters converges to a multivariate Gaussian distribution with identity covariance. This theoretical framework facilitates an efficient simultaneous testing procedure that incorporates multiple power parameters.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"214 ","pages":"Article 105619"},"PeriodicalIF":1.4,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147385531","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}
Pub Date : 2026-07-01Epub Date: 2026-02-25DOI: 10.1016/j.jmva.2026.105625
Jinwen Liang , Keming Yu , Jianxin Pan , Wolfgang Karl Härdle , Maozai Tian
Medical imaging serves as a vital tool for visualizing anatomical structures and physiological characteristics. Understanding the relationship between clinical variables and anatomical structures represents a crucial research direction in medical science. A popular approach involves tensor regression modeling, where medical images are treated as multidimensional responses while clinical variables serve as predictors. When analyzing the relationship between tensor response and vector predictor, few studies account for interactions. In this study we focus on a main effects and interactions tensor regression model, which takes tensors as response and vectors as covariates. The aim is to estimate the unknown tensor coefficient under strong hierarchy assumption. We propose a tensor on vector with interaction estimator (TOVI) and develop an alternating iterative algorithm to solve the resulting optimization problem. Statistical properties of the proposed estimator have been established. Simulations show TOVI outperforms multiple alternatives. The analysis of the Autism Brain Imaging Data Exchange (ABIDE) data on autism spectrum disorder (ASD) demonstrates the superiority of TOVI.
医学影像是可视化解剖结构和生理特征的重要工具。了解临床变量与解剖结构之间的关系是医学科学的一个重要研究方向。一种流行的方法涉及张量回归建模，其中医学图像被视为多维响应，而临床变量作为预测因子。在分析张量响应与矢量预测器之间的关系时，很少有研究考虑到相互作用。本文研究了一个以张量为响应、向量为协变量的主效应与交互张量回归模型。目的是在强层次假设下估计未知张量系数。我们提出了一个带交互估计量的矢量张量（TOVI），并开发了一个交替迭代算法来解决由此产生的优化问题。所提出的估计量的统计性质已被证实。仿真结果表明，TOVI优于多种替代方案。通过对自闭症谱系障碍（ASD）自闭症脑成像数据交换（Autism Brain Imaging Data Exchange，简称ABIDE）数据的分析，证明了TOVI的优越性。
{"title":"Tensor-on-vector regression with interactions with application to fMRI data","authors":"Jinwen Liang , Keming Yu , Jianxin Pan , Wolfgang Karl Härdle , Maozai Tian","doi":"10.1016/j.jmva.2026.105625","DOIUrl":"10.1016/j.jmva.2026.105625","url":null,"abstract":"<div><div>Medical imaging serves as a vital tool for visualizing anatomical structures and physiological characteristics. Understanding the relationship between clinical variables and anatomical structures represents a crucial research direction in medical science. A popular approach involves tensor regression modeling, where medical images are treated as multidimensional responses while clinical variables serve as predictors. When analyzing the relationship between tensor response and vector predictor, few studies account for interactions. In this study we focus on a main effects and interactions tensor regression model, which takes tensors as response and vectors as covariates. The aim is to estimate the unknown tensor coefficient under strong hierarchy assumption. We propose a tensor on vector with interaction estimator (TOVI) and develop an alternating iterative algorithm to solve the resulting optimization problem. Statistical properties of the proposed estimator have been established. Simulations show TOVI outperforms multiple alternatives. The analysis of the Autism Brain Imaging Data Exchange (ABIDE) data on autism spectrum disorder (ASD) demonstrates the superiority of TOVI.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"214 ","pages":"Article 105625"},"PeriodicalIF":1.4,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147385535","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}
Pub Date : 2026-07-01Epub Date: 2026-01-16DOI: 10.1016/j.jmva.2026.105606
Jia Zhou , Yang Li , Zemin Zheng , Changchun Tan
Reproducible learning of high-dimensional graphical structures is fundamentally important in numerous contemporary applications, as it visually reveals the underlying conditional dependencies among complex network data. In this paper, we introduce a novel procedure called the uniform graphical knockoff filter, which controls the overall false discovery rate (FDR) in Gaussian graph recovery by utilizing knockoff variables and a uniform threshold. Compared to existing methods, it is more robust to varying levels of sparsity in the true graph. We provide theoretical justifications for the procedure, demonstrating that the FDR can be asymptotically controlled and that the power is asymptotically one under mild conditions. Extensive numerical studies confirm the robust and competitive finite-sample performance of the proposed method.
{"title":"Uniform knockoff filter for high-dimensional controlled graph recovery","authors":"Jia Zhou , Yang Li , Zemin Zheng , Changchun Tan","doi":"10.1016/j.jmva.2026.105606","DOIUrl":"10.1016/j.jmva.2026.105606","url":null,"abstract":"<div><div>Reproducible learning of high-dimensional graphical structures is fundamentally important in numerous contemporary applications, as it visually reveals the underlying conditional dependencies among complex network data. In this paper, we introduce a novel procedure called the uniform graphical knockoff filter, which controls the overall false discovery rate (FDR) in Gaussian graph recovery by utilizing knockoff variables and a uniform threshold. Compared to existing methods, it is more robust to varying levels of sparsity in the true graph. We provide theoretical justifications for the procedure, demonstrating that the FDR can be asymptotically controlled and that the power is asymptotically one under mild conditions. Extensive numerical studies confirm the robust and competitive finite-sample performance of the proposed method.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"214 ","pages":"Article 105606"},"PeriodicalIF":1.4,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081119","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}
Pub Date : 2026-07-01Epub Date: 2026-01-27DOI: 10.1016/j.jmva.2026.105615
Zhiping Qiu , Wei Lin , Xiaming Tu , Jin-Ting Zhang
In many scientific and technological fields, multivariate functional data are often repeatedly observed under varying conditions over time. A fundamental question is whether the mean vector function remains consistently equal throughout the entire period. This paper introduces two novel global testing statistics that leverage integration technique to address this issue. The asymptotic distributions of the proposed test statistics under the null hypothesis are derived, and their root- consistency is established. Simulation studies are conducted to evaluate the numerical performance of the proposed tests, which are further illustrated through an analysis of publicly available EEG motion data.
{"title":"Global tests for detecting change in mean vector functions of multivariate functional data with repeated observations","authors":"Zhiping Qiu , Wei Lin , Xiaming Tu , Jin-Ting Zhang","doi":"10.1016/j.jmva.2026.105615","DOIUrl":"10.1016/j.jmva.2026.105615","url":null,"abstract":"<div><div>In many scientific and technological fields, multivariate functional data are often repeatedly observed under varying conditions over time. A fundamental question is whether the mean vector function remains consistently equal throughout the entire period. This paper introduces two novel global testing statistics that leverage integration technique to address this issue. The asymptotic distributions of the proposed test statistics under the null hypothesis are derived, and their root-<span><math><mi>n</mi></math></span> consistency is established. Simulation studies are conducted to evaluate the numerical performance of the proposed tests, which are further illustrated through an analysis of publicly available EEG motion data.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"214 ","pages":"Article 105615"},"PeriodicalIF":1.4,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081120","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}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号