Journal of Multivariate Analysis最新文献

Enhanced HSIC for independence test via projection integration 通过投影集成增强HSIC独立性测试

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2025-07-26 DOI: 10.1016/j.jmva.2025.105485

Zhimei Li , Tianxuan Ding , Tingyou Zhou , Yaowu Zhang

引用次数: 0

The multirank likelihood for semiparametric canonical correlation analysis 半参数典型相关分析的多秩似然

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2025-07-25 DOI: 10.1016/j.jmva.2025.105484

Jordan G. Bryan , Jonathan Niles-Weed , Peter D. Hoff

{"title":"The multirank likelihood for semiparametric canonical correlation analysis","authors":"Jordan G. Bryan , Jonathan Niles-Weed , Peter D. Hoff","doi":"10.1016/j.jmva.2025.105484","DOIUrl":"10.1016/j.jmva.2025.105484","url":null,"abstract":"<div><div>Many analyses of multivariate data focus on evaluating the dependence between two sets of variables, rather than the dependence among individual variables within each set. Canonical correlation analysis (CCA) is a classical data analysis technique that estimates parameters describing the dependence between such sets. However, inference procedures based on traditional CCA rely on the assumption that all variables are jointly normally distributed. We present a semiparametric approach to CCA in which the multivariate margins of each variable set may be arbitrary, but the dependence between variable sets is described by a parametric model that provides low-dimensional summaries of dependence. While maximum likelihood estimation in the proposed model is intractable, we propose two estimation strategies: one using a pseudolikelihood for the model and one using a Markov chain Monte Carlo (MCMC) algorithm that provides Bayesian estimates and confidence regions for the between-set dependence parameters. The MCMC algorithm is derived from a multirank likelihood function, which uses only part of the information in the observed data in exchange for being free of assumptions about the multivariate margins. We apply the proposed Bayesian inference procedure to Brazilian climate data and monthly stock returns from the materials and communications market sectors.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"210 ","pages":"Article 105484"},"PeriodicalIF":1.4,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144748754","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

Estimators for multivariate allometric regression model 多元异速回归模型的估计量

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2025-07-24 DOI: 10.1016/j.jmva.2025.105482

Koji Tsukuda , Shun Matsuura

{"title":"Estimators for multivariate allometric regression model","authors":"Koji Tsukuda , Shun Matsuura","doi":"10.1016/j.jmva.2025.105482","DOIUrl":"10.1016/j.jmva.2025.105482","url":null,"abstract":"<div><div>In a regression model with multiple response variables and multiple explanatory variables, if the difference of the mean vectors of the response variables for different values of explanatory variables is always in the direction of the first principal eigenvector of the covariance matrix of the response variables, then it is called a multivariate allometric regression model. This paper studies the estimation of the first principal eigenvector in the multivariate allometric regression model. A class of estimators that includes conventional estimators is proposed based on weighted sum-of-squares matrices of regression sum-of-squares matrix and residual sum-of-squares matrix. We establish an upper bound of the mean squared error of the estimators contained in this class, and the weight value minimizing the upper bound is derived. Sufficient conditions for the consistency of the estimators are discussed in weak identifiability regimes under which the difference of the largest and second largest eigenvalues of the covariance matrix decays asymptotically and in “large <span><math><mi>p</mi></math></span>, large <span><math><mi>n</mi></math></span>” regimes, where <span><math><mi>p</mi></math></span> is the number of response variables and <span><math><mi>n</mi></math></span> is the sample size. Several numerical results are also presented.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"210 ","pages":"Article 105482"},"PeriodicalIF":1.4,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144714168","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 properties of fractional posterior in generalized reduced-rank regression 广义约秩回归中分数后验的性质

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2025-07-21 DOI: 10.1016/j.jmva.2025.105481

The Tien Mai

{"title":"On properties of fractional posterior in generalized reduced-rank regression","authors":"The Tien Mai","doi":"10.1016/j.jmva.2025.105481","DOIUrl":"10.1016/j.jmva.2025.105481","url":null,"abstract":"<div><div>Reduced rank regression (RRR) is a widely employed model for investigating the linear association between multiple response variables and a set of predictors. While RRR has been extensively explored in various works, the focus has predominantly been on continuous response variables, overlooking other types of outcomes. This study shifts its attention to the Bayesian perspective of generalized linear models (GLM) within the RRR framework. In this work, we relax the requirement for the link function of the generalized linear model to be canonical. We examine the properties of fractional posteriors in GLM within the RRR context, where a fractional power of the likelihood is utilized. By employing a spectral scaled Student prior distribution, we establish consistency and concentration results for the fractional posterior. Our results highlight adaptability, as they do not necessitate prior knowledge of the rank of the parameter matrix. These results are in line with those found in frequentist literature. We also investigate the impact of model misspecification, demonstrating the robustness of our approach in such cases. Numerical simulations and real data analyses are conducted to illustrate the promising performance of our approach compared to the state-of-the-art method.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"210 ","pages":"Article 105481"},"PeriodicalIF":1.4,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144687416","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

Test for a general trilinear hypothesis in the generalized growth curve model 广义生长曲线模型中一般三线性假设的检验

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2025-07-16 DOI: 10.1016/j.jmva.2025.105470

Justine Dushimirimana , Isaac Kipchirchir Chumba , Lydia Musiga , Joseph Nzabanita , Ronald Waliaula Wanyonyi

{"title":"Test for a general trilinear hypothesis in the generalized growth curve model","authors":"Justine Dushimirimana , Isaac Kipchirchir Chumba , Lydia Musiga , Joseph Nzabanita , Ronald Waliaula Wanyonyi","doi":"10.1016/j.jmva.2025.105470","DOIUrl":"10.1016/j.jmva.2025.105470","url":null,"abstract":"<div><div>In this paper, we consider the problem of testing a general trilinear hypothesis in the generalized growth curve model. The general trilinear hypothesis was formulated to test for example the significance of the generalized growth curves or the equality of the trilinear mean between groups in the two dimensions. The null hypothesis considered is of the form <span><math><mrow><mi>ℬ</mi><mspace></mspace><mo>×</mo><mrow><mo>{</mo><mi>L</mi><mo>,</mo><mi>M</mi><mo>,</mo><mi>N</mi><mo>}</mo></mrow><mo>=</mo><mi>O</mi></mrow></math></span>, where <span><math><mrow><mi>L</mi><mo>,</mo><mi>M</mi></mrow></math></span> and <span><math><mi>N</mi></math></span> are known matrices, <span><math><mi>ℬ</mi></math></span> is unknown parameter tensor and <span><math><mi>O</mi></math></span> is a tensor of zeros. The estimators of the parameters were obtained using a flip-flop algorithm under the null and alternative hypotheses. The likelihood ratio test for testing the general trilinear hypothesis was discussed. The proposed test is an extension of the likelihood ratio test for the general linear hypothesis under the growth curve model. A simulation study was performed to evaluate the performance of the proposed test and a real dataset was used for an illustrative example.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"210 ","pages":"Article 105470"},"PeriodicalIF":1.4,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144662186","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

Generalized score matching 广义分数匹配

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2025-07-05 DOI: 10.1016/j.jmva.2025.105473

Jiazhen Xu, Janice L. Scealy, Andrew T.A. Wood, Tao Zou

引用次数: 0

Moment-type estimators for the Dirichlet and the multivariate gamma distributions 狄利克雷分布和多元伽玛分布的矩型估计

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2025-07-03 DOI: 10.1016/j.jmva.2025.105471

Ioannis Oikonomidis, Samis Trevezas

引用次数: 0

Robust functional inverse regression 稳健函数逆回归

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2025-07-02 DOI: 10.1016/j.jmva.2025.105472

Haoyang Cheng , Jianjun Xu , Qian Huang

{"title":"Robust functional inverse regression","authors":"Haoyang Cheng , Jianjun Xu , Qian Huang","doi":"10.1016/j.jmva.2025.105472","DOIUrl":"10.1016/j.jmva.2025.105472","url":null,"abstract":"<div><div>Functional sufficient dimension reduction (FSDR) is a popular approach for supervised dimensionality reduction in regression settings, as it allows for the reduction of functional predictors to a lower-dimensional subspace without loss of information. However, most existing FSDR methods are vulnerable to heavy-tailedness or outliers, which are common in many real-world applications. To address this limitation, we propose a robust FSDR method that utilizes a functional pairwise spatial sign (PASS) operator. This approach is suitable for both completely observed functional data and sparsely observed longitudinal data. Our method provides a more robust approach to FSDR, by taking into account the spatial information of the data and assigning greater weights to the less outlier-prone observations. We also provide a convergence rate analysis of the estimator, demonstrating that our method yields a consistent estimate of the dimension reduction directions. The effectiveness of our proposed method is demonstrated through extensive simulations and real data analysis. Our method outperforms existing methods in terms of robustness and accuracy, making it a valuable tool for analyzing functional data across various applications.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"210 ","pages":"Article 105472"},"PeriodicalIF":1.4,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144562999","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 signal recovery in Hadamard spaces Hadamard空间的鲁棒信号恢复

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2025-06-30 DOI: 10.1016/j.jmva.2025.105469

Georg Köstenberger, Thomas Stark

{"title":"Robust signal recovery in Hadamard spaces","authors":"Georg Köstenberger, Thomas Stark","doi":"10.1016/j.jmva.2025.105469","DOIUrl":"10.1016/j.jmva.2025.105469","url":null,"abstract":"<div><div>We analyze the stability of (strong) laws of large numbers in Hadamard spaces with respect to distributional perturbations. For the inductive means of a sequence of independent but not necessarily identically distributed random variables, we provide a concentration inequality in quadratic mean and a strong law of large numbers, generalizing a classical result of K.-T. Sturm. For the Fréchet mean, we generalize H. Ziezold’s law of large numbers in Hadamard spaces. In this case, we neither require our data to be independent nor identically distributed; reasonably mild conditions on the first two moments of our sample are enough. Additionally, we look at data contamination via a model inspired by Huber’s <span><math><mi>ɛ</mi></math></span>-contamination model, in which we replace a random portion of the data with noise. In the most general setup, we neither require the data nor the noise to be i.i.d., nor do we require the noise to be independent of the data. A resampling scheme is introduced to analyze the stability of the (non-symmetric) inductive mean with respect to data loss, data permutation, and noise, and sufficient conditions for its convergence are provided. These results suggest that means in Hadamard spaces are as robust as those in Euclidean spaces. This is underlined by a small simulation study in which we compare the robustness of means on the manifold of positive definite matrices with means on open books.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"210 ","pages":"Article 105469"},"PeriodicalIF":1.4,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144549931","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 factorization for high-dimensional matrix-variate observations 高维矩阵变量观测的鲁棒分解

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2025-06-26 DOI: 10.1016/j.jmva.2025.105467

Yalin Wang , Long Yu

{"title":"Robust factorization for high-dimensional matrix-variate observations","authors":"Yalin Wang , Long Yu","doi":"10.1016/j.jmva.2025.105467","DOIUrl":"10.1016/j.jmva.2025.105467","url":null,"abstract":"<div><div>Large-dimensional matrix-variate observations have been ubiquitous in the big data era, while unsupervised low-rank approximation technique would help reveal their hidden patterns and structures. In this paper, we study a hierarchical Canonical Polyadic (CP) product matrix factor model under the elliptical framework, which essentially assumes that the matrix-variate observations are from a matrix elliptical distribution. The proposed model not only incorporates the row-wise and column-wise interrelated information, but also adapts to the tail properties of the matrix-variate observations. We resort to the matrix Kendall’s tau introduced in the recent literature to recover the loading spaces, and minimize the square loss function to estimate the factor scores. We also propose an eigenvalue-ratio method to estimate the pair of factor numbers. Thorough theories for the model estimation, including statistical consistency and rates of convergence, are established under regular conditions. It is worth highlighting that the proposed method exhibits superior performance compared to other methods for estimating the signal part, particularly in the heavy-tailed cases. This superiority has been thoroughly validated through extensive simulations. The effectiveness in matrix reconstruction of the proposed method is demonstrated by applying it to a macroeconomic dataset of China.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"210 ","pages":"Article 105467"},"PeriodicalIF":1.4,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144501685","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