Journal of Multivariate Analysis最新文献_第3页

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

Error analysis for a statistical finite element method 统计有限元法的误差分析

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2025-06-25 DOI: 10.1016/j.jmva.2025.105468

Toni Karvonen , Fehmi Cirak , Mark Girolami

{"title":"Error analysis for a statistical finite element method","authors":"Toni Karvonen , Fehmi Cirak , Mark Girolami","doi":"10.1016/j.jmva.2025.105468","DOIUrl":"10.1016/j.jmva.2025.105468","url":null,"abstract":"<div><div>The recently proposed statistical finite element (statFEM) approach synthesises measurement data with finite element models and allows for making predictions about the unknown true system response. We provide a probabilistic error analysis for a prototypical statFEM setup based on a Gaussian process prior under the assumption that the noisy measurement data are generated by a deterministic true system response function that satisfies a second-order elliptic partial differential equation for an unknown true source term. In certain cases, properties such as the smoothness of the source term may be misspecified by the Gaussian process model. The error estimates we derive are for the expectation with respect to the measurement noise of the <span><math><msup><mrow><mi>L</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span>-norm of the difference between the true system response and the mean of the statFEM posterior. The estimates imply polynomial rates of convergence in the numbers of measurement points and finite element basis functions and depend on the Sobolev smoothness of the true source term and the Gaussian process model. A numerical example for Poisson’s equation is used to illustrate these theoretical results.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"210 ","pages":"Article 105468"},"PeriodicalIF":1.4,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144492150","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

Random projection-based response best-subset selector for ultra-high dimensional multivariate data 基于随机投影的超高维多变量数据响应最佳子集选择器

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2025-06-23 DOI: 10.1016/j.jmva.2025.105465

Jianhua Hu , Tao Li , Xiaoqian Liu , Xu Liu

引用次数: 0

Additive regression for Riemannian functional responses 黎曼函数响应的加性回归

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2025-06-17 DOI: 10.1016/j.jmva.2025.105466

Jeong Min Jeon, Germain Van Bever

引用次数: 0

Deconvolution density estimation on Lie groups without auxiliary data 无辅助数据李群的反褶积密度估计