Journal of Multivariate Analysis最新文献_第6页

Limiting spectral distribution of high-dimensional integrated covariance matrices based on high-frequency data with multiple transactions 基于多事务高频数据的高维积分协方差矩阵的谱限分布

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2026-03-01 Epub Date: 2025-11-28 DOI: 10.1016/j.jmva.2025.105568

Moming Wang , Ningning Xia , Yong Zhou

{"title":"Limiting spectral distribution of high-dimensional integrated covariance matrices based on high-frequency data with multiple transactions","authors":"Moming Wang , Ningning Xia , Yong Zhou","doi":"10.1016/j.jmva.2025.105568","DOIUrl":"10.1016/j.jmva.2025.105568","url":null,"abstract":"<div><div>Due to the heavy trading volume in financial markets and the limitations of recording mechanisms, the occurrence of multiple transactions during each recording period is a common feature of high-frequency data. This paper investigates how the number of such multiple transactions impacts the behavior of an averaged version of time-variation adjusted realized covariance (ATVA) matrix in a high-dimensional situation, where the number of stocks and the observation frequency go to infinity proportionally. By using random matrix theory, we derive the limiting spectral distribution (LSD) of ATVA matrices based on high-frequency multiple observations. We demonstrate how the LSD of ATVA matrices depends on the number of multiple transactions. The study of the LSD of random matrices is not only theoretically interesting in itself but also provides a better insight into the pre-averaging approach, which is widely used to deal with the microstructure noise. Furthermore, we investigate the limits of spiked eigenvalues of ATVA matrices when the covariance matrix of asset prices exhibits a spiked pattern. Finally, the theoretical results are supported by simulation studies.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"212 ","pages":"Article 105568"},"PeriodicalIF":1.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145681884","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

Threshold models for high-dimensional time series with network structure 具有网络结构的高维时间序列阈值模型

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2026-03-01 Epub Date: 2025-11-28 DOI: 10.1016/j.jmva.2025.105560

Chi Tim Ng , Chun Yip Yau , Yuanbo Li , Lei Qin

引用次数: 0

Robust bilinear factor analysis based on the matrix-variate t distribution 基于矩阵变量t分布的稳健双线性因子分析

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2026-03-01 Epub Date: 2025-11-28 DOI: 10.1016/j.jmva.2025.105575

Xuan Ma , Jianhua Zhao , Changchun Shang , Fen Jiang , Philip L.H. Yu

{"title":"Robust bilinear factor analysis based on the matrix-variate t distribution","authors":"Xuan Ma , Jianhua Zhao , Changchun Shang , Fen Jiang , Philip L.H. Yu","doi":"10.1016/j.jmva.2025.105575","DOIUrl":"10.1016/j.jmva.2025.105575","url":null,"abstract":"<div><div>Factor Analysis based on the multivariate <em>t</em> distribution (<em>t</em>FA) is a useful robust tool for extracting common factors on heavy-tailed or contaminated data. However, <em>t</em>FA is only applicable to vector data. When <em>t</em>FA is applied to matrix data, it is common to first vectorize the matrix observations. This introduces two challenges for <em>t</em>FA: (i) the inherent matrix structure of the data is broken, and (ii) robustness may be lost, as vectorized matrix data typically results in a high data dimension, which could easily lead to the breakdown of <em>t</em>FA. To address these issues, starting from the intrinsic matrix structure of matrix data, a novel robust factor analysis model, namely bilinear factor analysis built on the matrix-variate <em>t</em> distribution (<em>t</em>BFA), is proposed in this paper. The novelty is that it is capable of simultaneously extracting common factors for both row and column variables of interest on heavy-tailed or contaminated matrix data. Two efficient algorithms for maximum likelihood estimation of <em>t</em>BFA are developed. Closed-form expressions for the Fisher information matrix to calculate the accuracy of parameter estimates are derived. Empirical studies are conducted to understand the proposed <em>t</em>BFA model and compare it with related competitors. The results demonstrate the superiority and practicality of <em>t</em>BFA. Importantly, <em>t</em>BFA exhibits a significantly higher breakdown point than <em>t</em>FA, making it more suitable for matrix data.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"212 ","pages":"Article 105575"},"PeriodicalIF":1.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145681898","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

Estimation of tensor factor model by iterative least squares 张量因子模型的迭代最小二乘估计

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2026-03-01 Epub Date: 2025-11-28 DOI: 10.1016/j.jmva.2025.105557

Yong He , Yujie Hou , Yalin Wang , Wen-Xin Zhou

{"title":"Estimation of tensor factor model by iterative least squares","authors":"Yong He , Yujie Hou , Yalin Wang , Wen-Xin Zhou","doi":"10.1016/j.jmva.2025.105557","DOIUrl":"10.1016/j.jmva.2025.105557","url":null,"abstract":"<div><div>For large-dimensional tensor time series, dimension reduction plays a pivotal role. Tensor factor model depicts tensor-valued time series through a low-dimensional projection on a space of common factors, thereby achieving great dimension reduction and having a wide range of applications in economics and finance. In this paper, we propose a simple iterative least squares algorithm for estimating tensor factor model. We first estimate the latent common factors by using deterministic mode-<span><math><mi>k</mi></math></span> projection matrices and then estimate the loading matrices by minimizing the squared Frobenius loss function under certain identifiability conditions. The estimated loading matrices are further taken as new mode-<span><math><mi>k</mi></math></span> projection matrices, and the above update procedures are iteratively executed until convergence. We also propose a novel eigenvalue ratio method for estimating the number of factors and show the consistency of the estimators. Given the true number of factors, we theoretically establish the convergence rates of the estimated loading matrices and signal components at the <span><math><mi>s</mi></math></span>th iteration for any <span><math><mrow><mi>s</mi><mo>≥</mo><mn>1</mn></mrow></math></span>. Thorough numerical studies are conducted to investigate the finite-sample performance of the proposed method. Analyses of import-export transport networks and lung cancer histopathological image datasets illustrate the empirical usefulness of the proposed method.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"212 ","pages":"Article 105557"},"PeriodicalIF":1.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145616472","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

Symmetric Bernoulli distributions and minimal dependence copulas 对称伯努利分布和最小依赖公式

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2026-03-01 Epub Date: 2025-11-17 DOI: 10.1016/j.jmva.2025.105545

Alessandro Mutti, Patrizia Semeraro

引用次数: 0

Recent advances in principal component analysis for directional data 定向数据主成分分析的新进展

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2026-03-01 Epub Date: 2025-11-15 DOI: 10.1016/j.jmva.2025.105528

Anahita Nodehi , Meisam Moghimbeygi , Christophe Ley

{"title":"Recent advances in principal component analysis for directional data","authors":"Anahita Nodehi , Meisam Moghimbeygi , Christophe Ley","doi":"10.1016/j.jmva.2025.105528","DOIUrl":"10.1016/j.jmva.2025.105528","url":null,"abstract":"<div><div>The high dimensionality of the input data can pose multiple problems when implementing statistical techniques. The presence of many dimensions in the data can lead to challenges in visualizing the data, higher computational demands, and a higher probability of over-fitting or under-fitting in modeling. Furthermore, the curse of dimensionality contributes to these issues by stating that the necessary number of observations for accurate modeling increases exponentially as the number of dimensions increases. Dimension reduction tools help overcome this challenge. Principal Component Analysis (PCA) is the most widely used technique, intensively studied in classical linear spaces. However, in applied sciences such as biology, bioinformatics, astronomy and geology, there are many instances in which the data’s support are non-Euclidean spaces. In fact, the available data often include elements of Riemannian manifolds such as the unit circle, torus, sphere, and their extensions. Therefore, the terms “manifold-valued” or “directional” data are used in the literature for these situations. When dealing with directional data, the linear nature of PCA might pose a challenge to achieve accurate data reduction. This paper therefore reviews and investigates the methodological aspects of PCA on directional data and their practical applications.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"212 ","pages":"Article 105528"},"PeriodicalIF":1.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145537554","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

Asymptotics for a discounted systemic risk measure in a multi-dimensional risk model with dependent claim sizes and stochastic return 具有独立索赔规模和随机收益的多维风险模型中贴现系统风险测度的渐近性

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2026-03-01 Epub Date: 2025-11-28 DOI: 10.1016/j.jmva.2025.105569

Yang Yang , Zhenyuan Xu , Yahui Fan

{"title":"Asymptotics for a discounted systemic risk measure in a multi-dimensional risk model with dependent claim sizes and stochastic return","authors":"Yang Yang , Zhenyuan Xu , Yahui Fan","doi":"10.1016/j.jmva.2025.105569","DOIUrl":"10.1016/j.jmva.2025.105569","url":null,"abstract":"<div><div>Consider a multi-dimensional renewal risk model, in which an insurer simultaneously operates more than one line of business, and is allowed to make both risk-free and risky investments. The claim sizes from all business lines are triggered by a series of common shocks, whose arrival times constitute a renewal counting process. The price process of the investment portfolio is described as a geometric Lévy process. In this study, we consider two cases that the claim sizes from different business lines are asymptotically dependent or asymptotically independent. Under the framework of regular variation, we study the asymptotic behavior of a discounted systemic risk measure, which is proposed by Li (2022) to describe the instant expected shortfall of the insurer at the moment when one of its business lines suffers crisis. The obtained results indicate that the asymptotics for the discounted systemic risk measure depend on neither the price process of the investment nor the renewal shock-number process, and, in addition, the asymptotic independence also gives no contribution to the asymptotic formula.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"212 ","pages":"Article 105569"},"PeriodicalIF":1.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145681885","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

Uniform designs for experiments with branching and nested factors 具有分支和嵌套因素的实验的统一设计

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2026-03-01 Epub Date: 2025-12-01 DOI: 10.1016/j.jmva.2025.105576

Feng Yang , Zheng Zhou , Yongdao Zhou

引用次数: 0

Density and graph estimation with smoothing splines and conditional Gaussian graphical models 平滑样条和条件高斯图模型的密度和图估计

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2026-03-01 Epub Date: 2025-11-14 DOI: 10.1016/j.jmva.2025.105543

Runfei Luo , Anna Liu , Hao Dong , Yuedong Wang

引用次数: 0

Stochastic arrangement increasing property of skew-normal distributions 偏正态分布的随机排列递增特性