Journal of Multivariate Analysis最新文献_第5页

Online stochastic Newton methods for estimating the geometric median and applications 估计几何中值的在线随机牛顿方法及其应用

IF 1.6 3区数学

Journal of Multivariate Analysis Pub Date : 2024-03-19 DOI: 10.1016/j.jmva.2024.105313

Antoine Godichon-Baggioni , Wei Lu

引用次数: 0

A consistent test of equality of distributions for Hilbert-valued random elements 希尔伯特值随机元素分布相等的一致性检验

IF 1.6 3区数学

Journal of Multivariate Analysis Pub Date : 2024-03-15 DOI: 10.1016/j.jmva.2024.105312

Gil González–Rodríguez , Ana Colubi , Wenceslao González–Manteiga , Manuel Febrero–Bande

{"title":"A consistent test of equality of distributions for Hilbert-valued random elements","authors":"Gil González–Rodríguez , Ana Colubi , Wenceslao González–Manteiga , Manuel Febrero–Bande","doi":"10.1016/j.jmva.2024.105312","DOIUrl":"10.1016/j.jmva.2024.105312","url":null,"abstract":"<div><p>Two independent random elements taking values in a separable Hilbert space are considered. The aim is to develop a test with bootstrap calibration to check whether they have the same distribution or not. A transformation of both random elements into a new separable Hilbert space is considered so that the equality of expectations of the transformed random elements is equivalent to the equality of distributions. Thus, a bootstrap test procedure to check the equality of means can be used in order to solve the original problem. It will be shown that both the asymptotic and bootstrap approaches proposed are asymptotically correct and consistent. The results can be applied, for example, in functional data analysis. In practice, the test can be solved with simple operations in the original space without applying the mentioned transformation, which is used only to guarantee the theoretical results. Empirical results and comparisons with related methods support and complement the theory.</p></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0047259X24000198/pdfft?md5=6bc123f9555c6b95bcd432fc26329ddb&pid=1-s2.0-S0047259X24000198-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140152986","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

Change point analysis of functional variance function with stationary error 具有静态误差的函数方差函数的变化点分析

IF 1.6 3区数学

Journal of Multivariate Analysis Pub Date : 2024-03-11 DOI: 10.1016/j.jmva.2024.105311

Qirui Hu

引用次数: 0

On heavy-tailed risks under Gaussian copula: The effects of marginal transformation 高斯共轭下的重尾风险：边际转换的影响

IF 1.6 3区数学

Journal of Multivariate Analysis Pub Date : 2024-03-01 DOI: 10.1016/j.jmva.2024.105310

Bikramjit Das , Vicky Fasen-Hartmann

引用次数: 0

High-dimensional nonconvex LASSO-type M-estimators 高维非凸 LASSO 型 M 估计器

IF 1.6 3区数学

Journal of Multivariate Analysis Pub Date : 2024-02-27 DOI: 10.1016/j.jmva.2024.105303

Jad Beyhum , François Portier

引用次数: 0

Nonlinear sufficient dimension reduction for distribution-on-distribution regression 分布对分布回归的非线性充分降维

IF 1.6 3区数学

Journal of Multivariate Analysis Pub Date : 2024-02-27 DOI: 10.1016/j.jmva.2024.105302

Qi Zhang, Bing Li, Lingzhou Xue

引用次数: 0

Linearized maximum rank correlation estimation when covariates are functional 协变量为函数时的线性化最大秩相关估计

IF 1.6 3区数学

Journal of Multivariate Analysis Pub Date : 2024-02-24 DOI: 10.1016/j.jmva.2024.105301

Wenchao Xu , Xinyu Zhang , Hua Liang

引用次数: 0

Variable selection in multivariate regression models with measurement error in covariates 具有协变量测量误差的多元回归模型中的变量选择

IF 1.6 3区数学

Journal of Multivariate Analysis Pub Date : 2024-02-17 DOI: 10.1016/j.jmva.2024.105299

Jingyu Cui , Grace Y. Yi

{"title":"Variable selection in multivariate regression models with measurement error in covariates","authors":"Jingyu Cui , Grace Y. Yi","doi":"10.1016/j.jmva.2024.105299","DOIUrl":"https://doi.org/10.1016/j.jmva.2024.105299","url":null,"abstract":"<div><p>Multivariate regression models have been broadly used in analyzing data having multi-dimensional response variables. The use of such models is, however, impeded by the presence of measurement error and spurious variables. While data with such features are common in applications, there has been little work available concerning these features jointly. In this article, we consider variable selection under multivariate regression models with covariates subject to measurement error. To gain flexibility, we allow the dimensions of the covariate and response variables to be either fixed or diverging as the sample size increases. A new regularized method is proposed to handle both variable selection and measurement error effects for error-contaminated data. Our proposed penalized bias-corrected least squares method offers flexibility in selecting the penalty function from a class of functions with different features. Importantly, our method does not require full distributional assumptions for the associated variables, thereby broadening its applicability. We rigorously establish theoretical results and describe a computationally efficient procedure for the proposed method. Numerical studies confirm the satisfactory performance of the proposed method under finite settings, and also demonstrate deleterious effects of ignoring measurement error in inferential procedures.</p></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2024-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139998890","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

Latent model extreme value index estimation 潜在模型极值指数估算

IF 1.6 3区数学

Journal of Multivariate Analysis Pub Date : 2024-02-15 DOI: 10.1016/j.jmva.2024.105300

Joni Virta , Niko Lietzén , Lauri Viitasaari , Pauliina Ilmonen

引用次数: 0

Estimation of multiple networks with common structures in heterogeneous subgroups 估计异质分组中具有共同结构的多个网络

IF 1.6 3区数学

Journal of Multivariate Analysis Pub Date : 2024-02-13 DOI: 10.1016/j.jmva.2024.105298

Xing Qin , Jianhua Hu , Shuangge Ma , Mengyun Wu

{"title":"Estimation of multiple networks with common structures in heterogeneous subgroups","authors":"Xing Qin , Jianhua Hu , Shuangge Ma , Mengyun Wu","doi":"10.1016/j.jmva.2024.105298","DOIUrl":"https://doi.org/10.1016/j.jmva.2024.105298","url":null,"abstract":"<div><p>Network estimation has been a critical component of high-dimensional data analysis and can provide an understanding of the underlying complex dependence structures. Among the existing studies, Gaussian graphical models have been highly popular. However, they still have limitations due to the homogeneous distribution assumption and the fact that they are only applicable to small-scale data. For example, cancers have various levels of unknown heterogeneity, and biological networks, which include thousands of molecular components, often differ across subgroups while also sharing some commonalities. In this article, we propose a new joint estimation approach for multiple networks with unknown sample heterogeneity, by decomposing the Gaussian graphical model (GGM) into a collection of sparse regression problems. A reparameterization technique and a composite minimax concave penalty are introduced to effectively accommodate the specific and common information across the networks of multiple subgroups, making the proposed estimator significantly advancing from the existing heterogeneity network analysis based on the regularized likelihood of GGM directly and enjoying scale-invariant, tuning-insensitive, and optimization convexity properties. The proposed analysis can be effectively realized using parallel computing. The estimation and selection consistency properties are rigorously established. The proposed approach allows the theoretical studies to focus on independent network estimation only and has the significant advantage of being both theoretically and computationally applicable to large-scale data. Extensive numerical experiments with simulated data and the TCGA breast cancer data demonstrate the prominent performance of the proposed approach in both subgroup and network identifications.</p></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139749242","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