Journal of Multivariate Analysis最新文献

Minimaxity under the half-Cauchy prior

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2025-03-05 DOI: 10.1016/j.jmva.2025.105431

Yuzo Maruyama , Takeru Matsuda

引用次数: 0

On estimation and order selection for multivariate extremes via clustering 关于通过聚类进行多元极值估计和阶次选择

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2025-02-28 DOI: 10.1016/j.jmva.2025.105426

Shiyuan Deng , He Tang , Shuyang Bai

{"title":"On estimation and order selection for multivariate extremes via clustering","authors":"Shiyuan Deng , He Tang , Shuyang Bai","doi":"10.1016/j.jmva.2025.105426","DOIUrl":"10.1016/j.jmva.2025.105426","url":null,"abstract":"<div><div>We investigate the estimation of multivariate extreme models with a discrete spectral measure using spherical clustering techniques. The primary contribution involves devising a method for selecting the order, that is, the number of clusters. The method consistently identifies the true order, i.e., the number of spectral atoms, and enjoys intuitive implementation in practice. Specifically, we introduce an extra penalty term to the well-known simplified average silhouette width, which penalizes small cluster sizes and small dissimilarities between cluster centers. Consequently, we provide a consistent method for determining the order of a max-linear factor model, where a typical information-based approach is not viable. Our second contribution is a large-deviation-type analysis for estimating the discrete spectral measure through clustering methods, which serves as an assessment of the convergence quality of clustering-based estimation for multivariate extremes. Additionally, as a third contribution, we discuss how estimating the discrete measure can lead to parameter estimations of heavy-tailed factor models. We also present simulations and real-data studies that demonstrate order selection and factor model estimation.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"208 ","pages":"Article 105426"},"PeriodicalIF":1.4,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143579875","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

Set-valued expectiles for ordered data analysis

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2025-02-27 DOI: 10.1016/j.jmva.2025.105425

Andreas H. Hamel, Thi Khanh Linh Ha

引用次数: 0

Ledoit-Wolf linear shrinkage with unknown mean

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2025-02-25 DOI: 10.1016/j.jmva.2025.105429

Benoît Oriol , Alexandre Miot

引用次数: 0

Markov switching multiple-equation tensor regressions

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2025-02-24 DOI: 10.1016/j.jmva.2025.105427

Roberto Casarin , Radu V. Craiu , Qing Wang

{"title":"Markov switching multiple-equation tensor regressions","authors":"Roberto Casarin , Radu V. Craiu , Qing Wang","doi":"10.1016/j.jmva.2025.105427","DOIUrl":"10.1016/j.jmva.2025.105427","url":null,"abstract":"<div><div>A new flexible tensor model for multiple-equation regressions that accounts for latent regime changes is proposed. The model allows for dynamic coefficients and multi-dimensional covariates that vary across equations. The coefficients are driven by a common hidden Markov process that addresses structural breaks to enhance the model flexibility and preserve parsimony. A new soft PARAFAC hierarchical prior is introduced to achieve dimensionality reduction while preserving the structural information of the covariate tensor. The proposed prior includes a new multi-way shrinking effect to address over-parametrization issues while preserving interpretability and model tractability. Theoretical results are derived to help with the choice of the hyperparameters. An efficient Markov chain Monte Carlo (MCMC) algorithm based on random scan Gibbs and back-fitting strategy is designed with priority placed on computational scalability of the posterior sampling. The validity of the MCMC algorithm is demonstrated theoretically, and its computational efficiency is studied using numerical experiments in different parameter settings. The effectiveness of the model framework is illustrated using two original real data analyses. The proposed model exhibits superior performance compared to the current benchmark, Lasso regression.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"208 ","pages":"Article 105427"},"PeriodicalIF":1.4,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143510638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Graph-constrained analysis for multivariate functional data

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2025-02-24 DOI: 10.1016/j.jmva.2025.105428

Debangan Dey , Sudipto Banerjee , Martin A. Lindquist , Abhirup Datta

{"title":"Graph-constrained analysis for multivariate functional data","authors":"Debangan Dey , Sudipto Banerjee , Martin A. Lindquist , Abhirup Datta","doi":"10.1016/j.jmva.2025.105428","DOIUrl":"10.1016/j.jmva.2025.105428","url":null,"abstract":"<div><div>The manuscript considers multivariate functional data analysis with a known graphical model among the functional variables representing their conditional relationships (e.g., brain region-level fMRI data with a prespecified connectivity graph among brain regions). Functional Gaussian graphical models (GGM) used for analyzing multivariate functional data customarily estimate an unknown graphical model, and cannot preserve knowledge of a given graph. We propose a method for multivariate functional analysis that exactly conforms to a given inter-variable graph. We first show the equivalence between partially separable functional GGM and graphical Gaussian processes (GP), proposed recently for constructing optimal multivariate covariance functions that retain a given graphical model. The theoretical connection helps to design a new algorithm that leverages Dempster’s covariance selection for obtaining the maximum likelihood estimate of the covariance function for multivariate functional data under graphical constraints. We also show that the finite term truncation of functional GGM basis expansion used in practice is equivalent to a low-rank graphical GP, which is known to oversmooth marginal distributions. To remedy this, we extend our algorithm to better preserve marginal distributions while respecting the graph and retaining computational scalability. The benefits of the proposed algorithms are illustrated using empirical experiments and a neuroimaging application.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"207 ","pages":"Article 105428"},"PeriodicalIF":1.4,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143550673","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 a class of finite mixture models that includes hidden Markov models

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2025-02-15 DOI: 10.1016/j.jmva.2025.105423

Francesco Bartolucci , Silvia Pandolfi , Fulvia Pennoni

引用次数: 0

Statistical analysis of parsimonious high-order multivariate finite Markov chains based on sufficient statistics

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2025-02-15 DOI: 10.1016/j.jmva.2025.105422

Yuriy Kharin, Valeriy Voloshko

{"title":"Statistical analysis of parsimonious high-order multivariate finite Markov chains based on sufficient statistics","authors":"Yuriy Kharin, Valeriy Voloshko","doi":"10.1016/j.jmva.2025.105422","DOIUrl":"10.1016/j.jmva.2025.105422","url":null,"abstract":"<div><div>A new parsimonious <span><math><mrow><mi>MCSS</mi><mrow><mo>(</mo><mi>s</mi><mo>)</mo></mrow></mrow></math></span> (which stands for “Markov Chain of order <span><math><mi>s</mi></math></span> based on Sufficient Statistics”) model for multivariate discrete-valued time series is constructed. The <span><math><mrow><mi>MCSS</mi><mrow><mo>(</mo><mi>s</mi><mo>)</mo></mrow></mrow></math></span> model has sufficient statistics of a simple form based on multivariate frequencies of <span><math><mrow><mo>(</mo><mi>s</mi><mo>+</mo><mn>1</mn><mo>)</mo></mrow></math></span>-tuples for observed time series. Special cases of the <span><math><mrow><mi>MCSS</mi><mrow><mo>(</mo><mi>s</mi><mo>)</mo></mrow></mrow></math></span> model and their relations to the results known in the literature are discussed. The strong concavity property of the loglikelihood function and the uniqueness of the maximum likelihood estimator under mild regularity conditions are proven for the <span><math><mrow><mi>MCSS</mi><mrow><mo>(</mo><mi>s</mi><mo>)</mo></mrow></mrow></math></span> model. Forecasting statistics for the multivariate discrete-valued time series derived with the <span><math><mrow><mi>MCSS</mi><mrow><mo>(</mo><mi>s</mi><mo>)</mo></mrow></mrow></math></span> model are constructed. The developed theory is illustrated with computer experiments on simulated and real data.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"208 ","pages":"Article 105422"},"PeriodicalIF":1.4,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143508246","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 review of multivariate permutation tests: Findings and trends

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2025-02-05 DOI: 10.1016/j.jmva.2025.105421

Rosa Arboretti , Elena Barzizza , Nicoló Biasetton , Marta Disegna

引用次数: 0

Consistency of empirical distributions of sequences of graph statistics in networks with dependent edges

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2025-02-05 DOI: 10.1016/j.jmva.2025.105420

Jonathan R. Stewart

{"title":"Consistency of empirical distributions of sequences of graph statistics in networks with dependent edges","authors":"Jonathan R. Stewart","doi":"10.1016/j.jmva.2025.105420","DOIUrl":"10.1016/j.jmva.2025.105420","url":null,"abstract":"<div><div>One of the first steps in applications of statistical network analysis is frequently to produce summary charts of important features of the network. Many of these features take the form of sequences of graph statistics counting the number of realized events in the network, examples of which are degree distributions, edgewise shared partner distributions, and more. We provide conditions under which the empirical distributions of sequences of graph statistics are consistent in the <span><math><msub><mrow><mi>ℓ</mi></mrow><mrow><mi>∞</mi></mrow></msub></math></span>-norm in settings where edges in the network are dependent. We accomplish this task by deriving concentration inequalities that bound probabilities of deviations of graph statistics from the expected value under weak dependence conditions. We apply our concentration inequalities to empirical distributions of sequences of graph statistics and derive non-asymptotic bounds on the <span><math><msub><mrow><mi>ℓ</mi></mrow><mrow><mi>∞</mi></mrow></msub></math></span>-error which hold with high probability. Our non-asymptotic results are then extended to demonstrate uniform convergence almost surely in selected examples. We illustrate theoretical results through examples, simulation studies, and an application.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"207 ","pages":"Article 105420"},"PeriodicalIF":1.4,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143209494","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