Journal of Multivariate Analysis最新文献_第2页

Conformal prediction for multivariate responses with Euclidean likelihood 欧几里得似然多变量响应的保形预测

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2025-08-14 DOI: 10.1016/j.jmva.2025.105494

Feichen Gan, Yukun Liu

引用次数: 0

Finite mixture representations of zero-and-N-inflated distributions for count-compositional data 计数成分数据的零和n膨胀分布的有限混合表示

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2025-08-14 DOI: 10.1016/j.jmva.2025.105492

André F.B. Menezes , Andrew C. Parnell , Keefe Murphy

引用次数: 0

Schrödinger bridge based deep conditional generative learning Schrödinger基于桥的深度条件生成学习

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2025-08-09 DOI: 10.1016/j.jmva.2025.105486

Hanwen Huang , Manyu Huang

{"title":"Schrödinger bridge based deep conditional generative learning","authors":"Hanwen Huang , Manyu Huang","doi":"10.1016/j.jmva.2025.105486","DOIUrl":"10.1016/j.jmva.2025.105486","url":null,"abstract":"<div><div>Conditional generative models represent a significant advancement in machine learning, enabling controlled data synthesis by incorporating additional information into the generation process. In this work, we introduce a novel Schrödinger bridge-based deep generative method for learning conditional distributions. Our approach begins with a unit-time diffusion process governed by a stochastic differential equation (SDE) that evolves a fixed point at time <span><math><mrow><mi>t</mi><mo>=</mo><mn>0</mn></mrow></math></span> into a desired target conditional distribution at <span><math><mrow><mi>t</mi><mo>=</mo><mn>1</mn></mrow></math></span>. For effective implementation, we discretize the SDE using the Euler–Maruyama method, estimating the drift term nonparametrically with a deep neural network. We apply our method to both low-dimensional and high-dimensional conditional generation tasks. Numerical studies show that, although our method does not directly provide conditional density estimation, the samples generated exhibit higher quality than those from several existing methods. Furthermore, the generated samples can be effectively used to estimate the conditional density and related statistical quantities, such as the conditional mean and conditional standard deviation.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"210 ","pages":"Article 105486"},"PeriodicalIF":1.4,"publicationDate":"2025-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144829784","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

Likelihood ratio test for covariance matrix under multivariate t distribution with uncorrelated observations 不相关观测值下多变量t分布下协方差矩阵的似然比检验

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2025-08-08 DOI: 10.1016/j.jmva.2025.105490

Katarzyna Filipiak , Daniel Klein , Stepan Mazur , Malwina Mrowińska

{"title":"Likelihood ratio test for covariance matrix under multivariate t distribution with uncorrelated observations","authors":"Katarzyna Filipiak , Daniel Klein , Stepan Mazur , Malwina Mrowińska","doi":"10.1016/j.jmva.2025.105490","DOIUrl":"10.1016/j.jmva.2025.105490","url":null,"abstract":"<div><div>In this paper, estimators for the unknown parameters under two types of matrix-variate <span><math><mi>t</mi></math></span> distributions are determined, and their basic statistical properties, including bias and sufficiency, are investigated. These estimators are then applied to test hypotheses concerning the covariance structure of a multivariate <span><math><mi>t</mi></math></span> distribution associated with a collection of uncorrelated, though not necessarily independent, observation vectors, using two types of matrix-variate <span><math><mi>t</mi></math></span> distributions. A likelihood ratio test is proposed, and its distributional properties under the null hypothesis are examined, assuming either a fully specified covariance matrix or one specified up to a constant. Furthermore, it is demonstrated that the asymptotic distribution for the type I matrix-variate <span><math><mi>t</mi></math></span> distribution under both hypotheses coincides with that under the normality assumption. Finally, for testing a fully specified covariance matrix, the asymptotic distribution of the likelihood ratio test statistic is determined.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"210 ","pages":"Article 105490"},"PeriodicalIF":1.4,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144829785","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

Exact mean and covariance formulas after diagonal transformations of a multivariate normal 多元正态曲线对角变换后的精确均值和协方差公式

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2025-08-06 DOI: 10.1016/j.jmva.2025.105489

Rebecca Morrison , Estelle Basor

引用次数: 0

Inference for overparametrized hierarchical Archimedean copulas 超参数化层次阿基米德copulas的推理

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2025-08-05 DOI: 10.1016/j.jmva.2025.105483

Samuel Perreault , Yanbo Tang , Ruyi Pan , Nancy Reid

{"title":"Inference for overparametrized hierarchical Archimedean copulas","authors":"Samuel Perreault , Yanbo Tang , Ruyi Pan , Nancy Reid","doi":"10.1016/j.jmva.2025.105483","DOIUrl":"10.1016/j.jmva.2025.105483","url":null,"abstract":"<div><div>Hierarchical Archimedean copulas (HACs) are multivariate uniform distributions constructed by nesting Archimedean copulas into one another, and provide a flexible approach to modeling non-exchangeable data. However, this flexibility in the model structure may lead to over-fitting when the model estimation procedure is not performed properly. In this paper, we examine the problem of structure estimation and more generally on the selection of a parsimonious model from the hypothesis testing perspective. Formal tests for structural hypotheses concerning HACs have been lacking so far, most likely due to the restrictions on their associated parameter space which hinders the use of standard inference methodology. Building on previously developed asymptotic methods for these non-standard parameter spaces, we provide an asymptotic stochastic representation for the maximum likelihood estimators of (potentially) overparametrized HACs, which we then use to formulate a likelihood ratio test for certain common structural hypotheses. Additionally, we also derive analytical expressions for the first- and second-order partial derivatives of two-level HACs based on Clayton and Gumbel generators, as well as general numerical approximation schemes for the Fisher information matrix.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"210 ","pages":"Article 105483"},"PeriodicalIF":1.4,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144840792","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

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