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A latent space model for link prediction in statistical citation network 统计引文网络中链接预测的潜在空间模型
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.2025.105555
Rui Pan , Yuan Gao , Hansheng Wang
{"title":"A latent space model for link prediction in statistical citation network","authors":"Rui Pan ,&nbsp;Yuan Gao ,&nbsp;Hansheng Wang","doi":"10.1016/j.jmva.2025.105555","DOIUrl":"10.1016/j.jmva.2025.105555","url":null,"abstract":"<div><div>Link prediction is of vital importance in network analysis. In this work, we propose a novel latent space model for link prediction in a statistical citation network. Specifically, the model can incorporate the transitivity information of both the citation network and the author-paper network. In addition, nodal features are also taken into consideration and the pseudo maximum likelihood estimation of the corresponding parameter is developed. The asymptotic consistency is established and demonstrated through extensive simulation studies. Link prediction is then performed and the performance is compared among different methods. At last, a real citation network of statistics is analyzed.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"212 ","pages":"Article 105555"},"PeriodicalIF":1.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145681891","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 and testing for fixed effects partially linear nonparametric panel regression model with separable spatially and serially correlated error structure 具有可分离空间和序列相关误差结构的部分线性非参数面板回归模型的固定效应估计与检验
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.2025.105552
Shuangshuang Li , Jianbao Chen
{"title":"Estimation and testing for fixed effects partially linear nonparametric panel regression model with separable spatially and serially correlated error structure","authors":"Shuangshuang Li ,&nbsp;Jianbao Chen","doi":"10.1016/j.jmva.2025.105552","DOIUrl":"10.1016/j.jmva.2025.105552","url":null,"abstract":"<div><div>Panel data collected from “locations” may exhibit spatial and serial correlations. In order to study such spatial and serial correlations, and possible existing nonlinear relationships, a fixed effects partially linear nonparametric panel regression model with separable spatially and serially correlated error structure is introduced. We obtain profile quasi-maximum likelihood estimators (PQMLEs) of the unknowns. Furthermore, a generalized F-test called <span><math><msub><mrow><mi>F</mi></mrow><mrow><mi>N</mi><mi>T</mi></mrow></msub></math></span> is designed for assessing the reasonability of nonparametric component setting. Asymptotic properties of PQMLEs and <span><math><msub><mrow><mi>F</mi></mrow><mrow><mi>N</mi><mi>T</mi></mrow></msub></math></span> are provided under several conditions. Monte Carlo trials imply our estimators and test statistic exhibit good performance in finite samples and model misspecification may lead to substantial influence on the estimates of unknown parameters. The analysis of provincial housing price in China reveals the presence of nonlinear, spatial and serial correlation relationships.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"212 ","pages":"Article 105552"},"PeriodicalIF":1.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145681835","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
Parametric convergence rate of a non-parametric estimator in multivariate mixtures of power series distributions under conditional independence 条件无关下幂级数分布多元混合中非参数估计量的参数收敛速率
IF 1.4 3区 数学
Journal of Multivariate Analysis Pub Date : 2026-03-01 Epub Date: 2025-11-19 DOI: 10.1016/j.jmva.2025.105542
Fadoua Balabdaoui , Harald Besdziek , Yong Wang
{"title":"Parametric convergence rate of a non-parametric estimator in multivariate mixtures of power series distributions under conditional independence","authors":"Fadoua Balabdaoui ,&nbsp;Harald Besdziek ,&nbsp;Yong Wang","doi":"10.1016/j.jmva.2025.105542","DOIUrl":"10.1016/j.jmva.2025.105542","url":null,"abstract":"<div><div>The conditional independence assumption has recently appeared in a growing body of literature on the estimation of multivariate mixtures. We consider here conditionally independent multivariate mixtures of power series distributions with infinite support, to which belong Poisson, Geometric or Negative Binomial mixtures. We show that for all these mixtures, the non-parametric maximum likelihood estimator converges to the truth at the rate <span><math><mrow><msup><mrow><mrow><mo>(</mo><mo>ln</mo><mrow><mo>(</mo><mi>n</mi><mi>d</mi><mo>)</mo></mrow><mo>)</mo></mrow></mrow><mrow><mn>1</mn><mo>+</mo><mi>d</mi><mo>/</mo><mn>2</mn></mrow></msup><msup><mrow><mi>n</mi></mrow><mrow><mo>−</mo><mn>1</mn><mo>/</mo><mn>2</mn></mrow></msup></mrow></math></span> in the Hellinger distance, where <span><math><mi>n</mi></math></span> denotes the size of the observed sample and <span><math><mi>d</mi></math></span> represents the dimension of the mixture. Using this result, we then construct a new non-parametric estimator based on the maximum likelihood estimator that converges with the parametric rate <span><math><msup><mrow><mi>n</mi></mrow><mrow><mo>−</mo><mn>1</mn><mo>/</mo><mn>2</mn></mrow></msup></math></span> in all <span><math><msub><mrow><mi>ℓ</mi></mrow><mrow><mi>p</mi></mrow></msub></math></span>-distances, for <span><math><mrow><mi>p</mi><mo>≥</mo><mn>1</mn></mrow></math></span>. These convergences rates are supported by simulations and the theory is illustrated using the famous Vélib dataset of the bike sharing system of Paris. We also introduce a testing procedure for whether the conditional independence assumption is satisfied for a given sample. This testing procedure is applied for several multivariate mixtures, with varying levels of dependence, and is thereby shown to distinguish well between conditionally independent and dependent mixtures. Finally, we use this testing procedure to investigate whether conditional independence holds for Vélib dataset.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"212 ","pages":"Article 105542"},"PeriodicalIF":1.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145571181","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 latent factor model for high-dimensional binary data 高维二值数据的潜在因子模型
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.2025.105554
Jiaxin Shi , Yuan Gao , Rui Pan , Hansheng Wang
{"title":"A latent factor model for high-dimensional binary data","authors":"Jiaxin Shi ,&nbsp;Yuan Gao ,&nbsp;Rui Pan ,&nbsp;Hansheng Wang","doi":"10.1016/j.jmva.2025.105554","DOIUrl":"10.1016/j.jmva.2025.105554","url":null,"abstract":"<div><div>In this study, we develop a latent factor model for analyzing high-dimensional binary data. Specifically, a standard probit model is used to describe the regression relationship between the observed binary data and the continuous latent variables. Our method assumes that the dependency structure of the observed binary data can be fully captured by the continuous latent factors. To estimate the model, a moment-based estimation method is developed. The proposed method is able to deal with both discontinuity and high dimensionality. Most importantly, the asymptotic properties of the resulting estimators are rigorously established. Extensive simulation studies are presented to demonstrate the proposed methodology. A real dataset about product descriptions is analyzed for illustration.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"212 ","pages":"Article 105554"},"PeriodicalIF":1.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145616471","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
Testing and measuring the conditional mean (in)dependence for functional data by martingale difference-angle divergence 用鞅差角散度检验和测量函数数据的条件均值（in）依赖性
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.2025.105573
Tingyu Lai , Yingying Wang , Zhongzhan Zhang
{"title":"Testing and measuring the conditional mean (in)dependence for functional data by martingale difference-angle divergence","authors":"Tingyu Lai ,&nbsp;Yingying Wang ,&nbsp;Zhongzhan Zhang","doi":"10.1016/j.jmva.2025.105573","DOIUrl":"10.1016/j.jmva.2025.105573","url":null,"abstract":"<div><div>We proposed a new nonparametric method to test and measure conditional mean (in)dependence for functional data. This new metric has some appealing properties: it is nonnegative and equals to zero if and only if the conditional mean independence holds; it is invariant under linear transformations of the predictor; it does not require the moment condition for the predictor variable. Based on this measure, two test procedures for conditional mean independence are proposed for functional data. One uses a wild bootstrap while the other uses the limiting standard normal distribution. The tests are consistent and perform well in finite sample simulations. We further propose some requirements for a reasonable conditional mean dependence measure and demonstrate that our metric has those properties. A real data example is introduced to illustrate the application of the proposed method.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"212 ","pages":"Article 105573"},"PeriodicalIF":1.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145681892","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
Bayesian analysis of nonlinear structured latent factor models with a Gaussian process prior 具有高斯过程先验的非线性结构化潜在因素模型的贝叶斯分析
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.2025.105577
Yimang Zhang , Xiaorui Wang , Jian Qing Shi
{"title":"Bayesian analysis of nonlinear structured latent factor models with a Gaussian process prior","authors":"Yimang Zhang ,&nbsp;Xiaorui Wang ,&nbsp;Jian Qing Shi","doi":"10.1016/j.jmva.2025.105577","DOIUrl":"10.1016/j.jmva.2025.105577","url":null,"abstract":"<div><div>Factor analysis models are widely used in social and behavioral sciences, such as psychology, education, and marketing, to measure unobservable latent traits. In this article, we introduce a nonlinear structured latent factor analysis model that is more flexible in characterizing the relationship between manifest variables and latent factors. The confirmatory identifiability of the latent factor is discussed, ensuring the substantive interpretation of these latent factors. A Bayesian approach with a Gaussian process prior is proposed to estimate the unknown nonlinear function and the unknown parameters. Asymptotic results are established, including the structured identifiability of latent factors, as well as the consistency of estimates for the unknown parameters and the unknown nonlinear function. Simulation studies and real data analysis are conducted to evaluate the performance of the proposed method. The simulation results demonstrate that our proposed method performs well in handling nonlinear model and successfully identifies the latent factors. Additionally, the analysis of oil flow data reveals the underlying structure of latent nonlinear patterns.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"212 ","pages":"Article 105577"},"PeriodicalIF":1.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145681893","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
Recovering Imbalanced Clusters via gradient-based projection pursuit 基于梯度投影追踪的不平衡簇恢复方法
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.2025.105530
Martin Eppert , Satyaki Mukherjee , Debarghya Ghoshdastidar
{"title":"Recovering Imbalanced Clusters via gradient-based projection pursuit","authors":"Martin Eppert ,&nbsp;Satyaki Mukherjee ,&nbsp;Debarghya Ghoshdastidar","doi":"10.1016/j.jmva.2025.105530","DOIUrl":"10.1016/j.jmva.2025.105530","url":null,"abstract":"<div><div>Projection Pursuit is a classic exploratory technique for finding interesting projections of a dataset. We propose a method for recovering projections containing either Imbalanced Clusters or a Bernoulli–Rademacher distribution using a gradient-based technique to optimize the projection index. As sample complexity is a major limiting factor in Projection Pursuit, we analyze our algorithm’s sample complexity within a Planted Vector setting where we can observe that Imbalanced Clusters can be recovered more easily than balanced ones. Additionally, we give a generalized result that works for a variety of data distributions and projection indices. We compare these results to computational lower bounds in the Low-Degree-Polynomial Framework. Finally, we experimentally evaluate our method’s applicability to real-world data using FashionMNIST and the Human Activity Recognition Dataset, where our algorithm outperforms others when only a few samples are available.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"212 ","pages":"Article 105530"},"PeriodicalIF":1.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145616469","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 two-way dimension reduction by Grassmannian barycenter 格拉斯曼质心鲁棒双向降维
IF 1.4 3区 数学
Journal of Multivariate Analysis Pub Date : 2026-03-01 Epub Date: 2025-11-08 DOI: 10.1016/j.jmva.2025.105527
Zeyu Li , Yong He , Xinbing Kong , Xinsheng Zhang
{"title":"Robust two-way dimension reduction by Grassmannian barycenter","authors":"Zeyu Li ,&nbsp;Yong He ,&nbsp;Xinbing Kong ,&nbsp;Xinsheng Zhang","doi":"10.1016/j.jmva.2025.105527","DOIUrl":"10.1016/j.jmva.2025.105527","url":null,"abstract":"<div><div>Two-way dimension reduction for well-structured matrix-valued data is growing popular in the past few years. To achieve robustness against individual matrix outliers with large spikes, arising either from heavy-tailed noise or large individual low-rank signals deviating from the population subspace, we first calculate the leading singular subspaces of each individual matrix, and then find the barycenter of the locally estimated subspaces across all observations, in contrast to the existing methods which first integrate data across observations and then do eigenvalue decomposition. In addition, a robust cut-off dimension determination criteria is suggested based on comparing the eigenvalue ratios of the corresponding Euclidean means of the projection matrices. Theoretical properties of the resulting estimators are investigated under mild conditions. Numerical simulation studies justify the advantages and robustness of the proposed methods over the existing tools. Two real examples associated with medical imaging and financial portfolios are given to provide empirical evidence on our arguments and also to illustrate the usefulness of the algorithms.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"212 ","pages":"Article 105527"},"PeriodicalIF":1.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145616470","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 convergence of regularized covariance estimator based on modified Cholesky decomposition 基于修正Cholesky分解的正则化协方差估计的收敛性
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.2025.105553
Yuli Liang , Deliang Dai , Shaobo Jin
{"title":"On convergence of regularized covariance estimator based on modified Cholesky decomposition","authors":"Yuli Liang ,&nbsp;Deliang Dai ,&nbsp;Shaobo Jin","doi":"10.1016/j.jmva.2025.105553","DOIUrl":"10.1016/j.jmva.2025.105553","url":null,"abstract":"<div><div>The regularization for covariance matrix is a widely used technique when estimating large covariance matrices. This paper examines a penalized likelihood method for constructing a statistically efficient covariance matrix estimator. Modified Cholesky decomposition (MCD) is used to parameterize the covariance matrix and the effective regularization scheme is achieved by combining both shrinkage and smoothing penalties on the Cholesky factor. The practical performance is at odds with an absence of theoretical properties of the derived estimators in the literature. In this work, we aim to fill the gap between theory and practice by establishing the convergence properties under regularity conditions. We also provide a simulation study as numerical illustrations.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"212 ","pages":"Article 105553"},"PeriodicalIF":1.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145681896","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
Distributed estimation of spiked eigenvalues in spiked population models 尖峰种群模型中尖峰特征值的分布估计
IF 1.4 3区 数学
Journal of Multivariate Analysis Pub Date : 2026-03-01 Epub Date: 2025-11-29 DOI: 10.1016/j.jmva.2025.105558
Lu Yan , Jiang Hu
{"title":"Distributed estimation of spiked eigenvalues in spiked population models","authors":"Lu Yan ,&nbsp;Jiang Hu","doi":"10.1016/j.jmva.2025.105558","DOIUrl":"10.1016/j.jmva.2025.105558","url":null,"abstract":"<div><div>The proliferation of science and technology has led to the prevalence of voluminous data sets distributed across multiple machines. Conventional statistical methodologies may be infeasible in analyzing such massive data sets due to prohibitively long computing durations, memory constraints, communication overheads, and confidentiality considerations. In this paper, we propose distributed estimators of the spiked eigenvalues in spiked population models. The consistency and asymptotic normality of the distributed estimators are derived, and the statistical error analysis of the distributed estimators is also provided. Compared to the estimation from the full sample, the proposed distributed estimation shares the same order of convergence. Simulation study and real data analysis indicate that the proposed distributed estimation and testing procedures have excellent properties in terms of estimation accuracy and stability as well as transmission efficiency.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"212 ","pages":"Article 105558"},"PeriodicalIF":1.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145681899","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
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