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Estimation for partially time-varying spatial autoregressive panel data model under linear constraints 线性约束下部分时变空间自回归面板数据模型的估计
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.2025.105547
Lingling Tian , Chuanhua Wei , Bing Sun , Mixia Wu
{"title":"Estimation for partially time-varying spatial autoregressive panel data model under linear constraints","authors":"Lingling Tian ,&nbsp;Chuanhua Wei ,&nbsp;Bing Sun ,&nbsp;Mixia Wu","doi":"10.1016/j.jmva.2025.105547","DOIUrl":"10.1016/j.jmva.2025.105547","url":null,"abstract":"<div><div>This paper investigates a constrained spatial autoregressive panel data model with fixed effects, partially linear time-varying coefficients, and time-varying spatial dependence. We propose a constrained profile two-stage least squares estimator and establish its asymptotic properties. Furthermore, a statistical test is constructed to examine whether the constant coefficients satisfy pre-specified linear constraints. Monte Carlo simulations under both independent and <span><math><mi>α</mi></math></span>-mixing error structures demonstrate the finite-sample performance of the proposed estimators and testing procedure. A real data example is provided to illustrate the practical applicability of the method. In addition, when the time dimension <span><math><mi>T</mi></math></span> is relatively small, a Block Bootstrap procedure is proposed to compute the <span><math><mi>p</mi></math></span>-value for the test.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"212 ","pages":"Article 105547"},"PeriodicalIF":1.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145571182","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
Varying-coefficient quantile regression with effect under panel data and missing observation 在面板数据和缺失观测下的变系数分位数回归
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.2025.105561
Shu-Yu Li , Han-Ying Liang , Bao-Hua Wang
{"title":"Varying-coefficient quantile regression with effect under panel data and missing observation","authors":"Shu-Yu Li ,&nbsp;Han-Ying Liang ,&nbsp;Bao-Hua Wang","doi":"10.1016/j.jmva.2025.105561","DOIUrl":"10.1016/j.jmva.2025.105561","url":null,"abstract":"<div><div>We, in this paper, focus on partial linear varying-coefficient quantile regression with fixed effects under panel data and missing observations, where the missing observations include either responses or covariates are missing at random. Under independent setting, we define estimators of the unknown parameter vector, varying-coefficient function and effect in the model, and discuss their large number properties. To use the information from within-subject correlations, we propose weighted estimators for the unknown amounts, where the weights are chosen based on mean and quantile regressions, respectively, by quadratic inference technique and empirical likelihood method. Under dependent assumption, we establish asymptotic normality of the weighted estimators. Meanwhile, we study hypothesis tests of the parameter, varying-coefficient function and effect, and prove asymptotic distributions of restricted estimators and test statistics of the parameter under null hypothesis and local alternative hypothesis, respectively. Also, oracle property of the parameter is considered. Simulation study and real data analysis are conducted to evaluate the performance of the proposed methods.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"212 ","pages":"Article 105561"},"PeriodicalIF":1.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145681897","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 componentwise estimation procedure for multivariate location and scatter: Robustness, efficiency and scalability 多变量定位和分散的组件估计方法：鲁棒性、效率和可扩展性
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.2025.105546
Soumya Chakraborty , Ayanendranath Basu , Abhik Ghosh
{"title":"A componentwise estimation procedure for multivariate location and scatter: Robustness, efficiency and scalability","authors":"Soumya Chakraborty ,&nbsp;Ayanendranath Basu ,&nbsp;Abhik Ghosh","doi":"10.1016/j.jmva.2025.105546","DOIUrl":"10.1016/j.jmva.2025.105546","url":null,"abstract":"<div><div>Covariance matrix estimation is an important problem in multivariate data analysis, both from theoretical as well as applied points of view. Many simple and popular covariance matrix estimators are known to be severely affected by model misspecification and the presence of outliers in the data; on the other hand robust estimators with reasonably high efficiency are often computationally challenging for modern large and complex datasets. In this work, we propose a new, simple, robust and highly efficient method for estimation of the location vector and the scatter matrix for elliptically symmetric distributions. The proposed estimation procedure is designed in the spirit of the minimum density power divergence (DPD) estimation approach with appropriate modifications which makes our proposal (componentwise minimum DPD estimation) computationally very economical and scalable to large as well as higher dimensional datasets. Consistency and asymptotic normality of the proposed componentwise estimators of the multivariate location and scatter are established along with asymptotic positive definiteness of the estimated scatter matrix. Robustness of our estimators are studied by means of influence functions. All theoretical results are illustrated further under multivariate normality. A large-scale simulation study is presented to assess finite sample performances and scalability of our method in comparison to the usual maximum likelihood estimator (MLE), the ordinary minimum DPD estimator (MDPDE) and other popular non-parametric methods. The applicability of our method is further illustrated with a real dataset on credit card transactions.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"212 ","pages":"Article 105546"},"PeriodicalIF":1.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145616468","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
Fréchet kNN-based sufficient dimension reduction 基于knn的fresamet充分降维
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.2025.105566
Xueyan Huang, Rui Qiu, Zhou Yu
{"title":"Fréchet kNN-based sufficient dimension reduction","authors":"Xueyan Huang,&nbsp;Rui Qiu,&nbsp;Zhou Yu","doi":"10.1016/j.jmva.2025.105566","DOIUrl":"10.1016/j.jmva.2025.105566","url":null,"abstract":"<div><div>In this paper, we introduce two Fréchet inverse regression methods with kernel bandwidths determined by <span><math><mi>k</mi></math></span> nearest neighbors, designed to achieve sufficient dimension reduction for a metric space-valued response and Euclidean predictors. A key advantage of the proposals lies in their ability to effectively preserve the intrinsic information of the metric space-valued response. We establish the asymptotic normality of these methods through rigorous theoretical proofs. Additionally, simulations and a real data example are provided to validate the performance and practical applicability of the proposed methods.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"212 ","pages":"Article 105566"},"PeriodicalIF":1.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145681887","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
Type I multivariate Pólya-Aeppli distributions with applications 带应用程序的I型多变量Pólya-Aeppli发行版
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.2025.105556
Claire Geldenhuys, Rene Ehlers, Andriette Bekker
{"title":"Type I multivariate Pólya-Aeppli distributions with applications","authors":"Claire Geldenhuys,&nbsp;Rene Ehlers,&nbsp;Andriette Bekker","doi":"10.1016/j.jmva.2025.105556","DOIUrl":"10.1016/j.jmva.2025.105556","url":null,"abstract":"<div><div>An extensive body of literature exists that specifically addresses the univariate case of zero-inflated count models. In contrast, research pertaining to multivariate models is notably less developed. We propose two new parsimonious multivariate models that can be used to model correlated multivariate overdispersed count data. Furthermore, for different parameter settings and sample sizes, various simulations are performed. In conclusion, we demonstrate the performance of the newly proposed multivariate candidates on two benchmark datasets, which surpasses that of several alternative approaches.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"212 ","pages":"Article 105556"},"PeriodicalIF":1.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145681888","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 the two-sample Behrens–Fisher problem for high-dimensional data 高维数据的双样本Behrens-Fisher问题
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.2025.105572
Yongshuai Chen , Gongming Shi , Xiaomeng Yan, Baoxue Zhang
{"title":"On the two-sample Behrens–Fisher problem for high-dimensional data","authors":"Yongshuai Chen ,&nbsp;Gongming Shi ,&nbsp;Xiaomeng Yan,&nbsp;Baoxue Zhang","doi":"10.1016/j.jmva.2025.105572","DOIUrl":"10.1016/j.jmva.2025.105572","url":null,"abstract":"<div><div>In this paper, we study the limiting distribution of Chen-Qin’s test statistic and propose a novel weighted bootstrap test procedure for the high-dimensional two-sample Behrens–Fisher problem. We first show that the test statistic has an asymptotic null that is a mixture of a chi-square-type mixture distribution and a normal distribution, without imposing either the normal assumption or a factor-like model assumption on the underlying distributions. To gain insight into the asymptotic null distribution of the test statistic, we show that under stronger restrictions on the covariance matrices and the null hypothesis, the test statistic is either asymptotically normal or a chi-square-type mixture distribution. The power properties of the test are evaluated asymptotically under the high-dimensional local and fixed alternative hypothesis. We also derive that the proposed weighted bootstrap test procedure has correct test level asymptotically. Two simulation studies and a real data example show that the new weighted bootstrap procedure significantly outperforms other benchmarks in terms of size control and is comparable in terms of power.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"212 ","pages":"Article 105572"},"PeriodicalIF":1.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145681890","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
Jump detection in single-index models with measurement error 具有测量误差的单指标模型的跳跃检测
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.2025.105574
Yuan Liu , Yan-Yong Zhao , Noriszura Ismail , Razik Ridzuan Mohd Tajuddin , Yuchun Zhang
{"title":"Jump detection in single-index models with measurement error","authors":"Yuan Liu ,&nbsp;Yan-Yong Zhao ,&nbsp;Noriszura Ismail ,&nbsp;Razik Ridzuan Mohd Tajuddin ,&nbsp;Yuchun Zhang","doi":"10.1016/j.jmva.2025.105574","DOIUrl":"10.1016/j.jmva.2025.105574","url":null,"abstract":"<div><div>Measurement error regression is widely used in statistical modeling. When the regression function is discontinuous, the estimation and inference have become challenging. In this paper, we develop a jump detection framework for a single index model with measurement error. First, for the single index model with measurement error, the consistent estimator of the index coefficient is obtained by using both the SIMEX (simulation extrapolation) and estimation equation methods. Then, the one-sided kernel local linear method is used to construct the estimator of the nonparametric function and the estimator of the jump point. Under some regularity assumptions, the asymptotic properties of the resultant estimators are established. The finite sample performance of our methodologies is evaluated by numerical simulation, and finally they are used to analyze the effect of serum cholesterol level and age on male blood.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"212 ","pages":"Article 105574"},"PeriodicalIF":1.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145616473","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 multivariate normality for two-level structural equation models 两级结构方程模型的多元正态性检验
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.2025.105562
Jiajuan Liang , Peter M. Bentler , Yiwen Cao
{"title":"Testing multivariate normality for two-level structural equation models","authors":"Jiajuan Liang ,&nbsp;Peter M. Bentler ,&nbsp;Yiwen Cao","doi":"10.1016/j.jmva.2025.105562","DOIUrl":"10.1016/j.jmva.2025.105562","url":null,"abstract":"<div><div>Multivariate normality is a common assumption in the maximum likelihood analysis of two-level structural equation models. Under the normal assumption, the independence condition on level-1 observations is no longer satisfied. As a result, existing statistics for testing multivariate normality based independent observations cannot be directly used for the same purpose in two-level structural equation models. In this paper we tackle this problem by employing the theory of spherical matrix distributions and some properties of invariant statistics. A series of necessary tests are constructed from some existing invariant statistics with a balanced level-1 sample design. These necessary tests are applicable without requiring a large level-1 or level-2 sample size. A Monte Carlo study is carried out to demonstrate the performance of the proposed tests in the aspects of controlling type I error rates, the power against a departure from multivariate normality for level-1 variables, and the power against a departure from multivariate normality for level-2 variables. An application of the necessary tests to a practical data set is illustrated.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"212 ","pages":"Article 105562"},"PeriodicalIF":1.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145616475","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
Wasserstein projection pursuit of non-Gaussian signals 非高斯信号的Wasserstein投影追踪
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.2025.105535
Satyaki Mukherjee , Soumendu Sundar Mukherjee , Debarghya Ghoshdastidar
{"title":"Wasserstein projection pursuit of non-Gaussian signals","authors":"Satyaki Mukherjee ,&nbsp;Soumendu Sundar Mukherjee ,&nbsp;Debarghya Ghoshdastidar","doi":"10.1016/j.jmva.2025.105535","DOIUrl":"10.1016/j.jmva.2025.105535","url":null,"abstract":"<div><div>We consider the general dimensionality reduction problem of locating in a high-dimensional data cloud, a <span><math><mi>k</mi></math></span>-dimensional non-Gaussian subspace of interesting features. We use a projection pursuit approach—we search for mutually orthogonal unit directions which maximise the <span><math><mi>q</mi></math></span>-Wasserstein distance of the empirical distribution of data-projections along these directions from a standard Gaussian. Under a generative model, where there is a underlying (unknown) low-dimensional non-Gaussian subspace, we prove rigorous statistical guarantees on the accuracy of approximating this unknown subspace by the directions found by our projection pursuit approach. Our results operate in the regime where the data dimensionality is comparable to the sample size, and thus supplement the recent literature on the non-feasibility of locating interesting directions via projection pursuit in the complementary regime where the data dimensionality is much larger than the sample size.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"212 ","pages":"Article 105535"},"PeriodicalIF":1.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145537558","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
Nonlinear functional principal component analysis using neural networks 基于神经网络的非线性泛函主成分分析
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.2025.105526
Rou Zhong , Jingxiao Zhang , Chunming Zhang
{"title":"Nonlinear functional principal component analysis using neural networks","authors":"Rou Zhong ,&nbsp;Jingxiao Zhang ,&nbsp;Chunming Zhang","doi":"10.1016/j.jmva.2025.105526","DOIUrl":"10.1016/j.jmva.2025.105526","url":null,"abstract":"<div><div>Functional principal component analysis (FPCA) is an important technique for dimension reduction in functional data analysis (FDA). Classical FPCA method is based on the Karhunen-Loève expansion, which assumes a linear structure of the observed functional data. However, the assumption may not always be satisfied, and the FPCA method can become inefficient when the data deviates from the linear assumption. In this paper, we propose a novel FPCA method that is suitable for data with a nonlinear structure with the use of neural networks. We construct networks that can be applied to functional data and explore the corresponding universal approximation property. The main use of our proposed nonlinear FPCA method is curve reconstruction. We conduct a simulation study to evaluate the performance of our method. The proposed method is also applied to a real-world data set to further demonstrate its superiority.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"212 ","pages":"Article 105526"},"PeriodicalIF":1.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145537559","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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