Journal of Multivariate Analysis最新文献_第6页

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

A data depth based nonparametric test of independence between two random vectors 基于数据深度的两随机向量独立性非参数检验

IF 1.6 3区数学

Journal of Multivariate Analysis Pub Date : 2024-02-08 DOI: 10.1016/j.jmva.2024.105297

Sakineh Dehghan, Mohammad Reza Faridrohani

引用次数: 0

Convergence analysis of data augmentation algorithms for Bayesian robust multivariate linear regression with incomplete data 不完整数据下贝叶斯稳健多元线性回归数据增强算法的收敛性分析

IF 1.6 3区数学

Journal of Multivariate Analysis Pub Date : 2024-01-20 DOI: 10.1016/j.jmva.2024.105296

Haoxiang Li, Qian Qin, Galin L. Jones

{"title":"Convergence analysis of data augmentation algorithms for Bayesian robust multivariate linear regression with incomplete data","authors":"Haoxiang Li, Qian Qin, Galin L. Jones","doi":"10.1016/j.jmva.2024.105296","DOIUrl":"10.1016/j.jmva.2024.105296","url":null,"abstract":"<div><p><span>Gaussian mixtures are commonly used for modeling heavy-tailed error distributions in robust linear regression. Combining the likelihood of a multivariate robust linear regression model with a standard improper prior distribution yields an analytically intractable posterior distribution<span> that can be sampled using a data augmentation algorithm. When the response matrix has missing entries, there are unique challenges to the application and analysis of the convergence properties of the algorithm. Conditions for geometric </span></span>ergodicity<span> are provided when the incomplete data have a “monotone” structure. In the absence of a monotone structure, an intermediate imputation step is necessary for implementing the algorithm. In this case, we provide sufficient conditions for the algorithm to be Harris ergodic. Finally, we show that, when there is a monotone structure and intermediate imputation is unnecessary, intermediate imputation slows the convergence of the underlying Monte Carlo Markov chain, while post hoc imputation does not. An R package for the data augmentation algorithm is provided.</span></p></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2024-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139509069","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