Journal of Multivariate Analysis最新文献_第3页

Two-sample test for high-dimensional covariance matrices: A normal-reference approach 高维协方差矩阵的双样本检验：正态参照方法

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2024-07-20 DOI: 10.1016/j.jmva.2024.105354

Jingyi Wang , Tianming Zhu , Jin-Ting Zhang

{"title":"Two-sample test for high-dimensional covariance matrices: A normal-reference approach","authors":"Jingyi Wang , Tianming Zhu , Jin-Ting Zhang","doi":"10.1016/j.jmva.2024.105354","DOIUrl":"10.1016/j.jmva.2024.105354","url":null,"abstract":"<div><p>Testing the equality of the covariance matrices of two high-dimensional samples is a fundamental inference problem in statistics. Several tests have been proposed but they are either too liberal or too conservative when the required assumptions are not satisfied which attests that they are not always applicable in real data analysis. To overcome this difficulty, a normal-reference test is proposed and studied in this paper. It is shown that under some regularity conditions and the null hypothesis, the proposed test statistic and a chi-squared-type mixture have the same limiting distribution. It is then justified to approximate the null distribution of the proposed test statistic using that of the chi-squared-type mixture. The distribution of the chi-squared-type mixture can be well approximated using a three-cumulant matched chi-squared-approximation with its approximation parameters consistently estimated from the data. The asymptotic power of the proposed test under a local alternative is also established. Simulation studies and a real data example demonstrate that the proposed test works well in general scenarios and outperforms the existing competitors substantially in terms of size control.</p></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141871513","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

An approximation to peak detection power using Gaussian random field theory 使用高斯随机场理论的峰值检测功率近似值

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2024-07-17 DOI: 10.1016/j.jmva.2024.105346

Yu Zhao , Dan Cheng , Armin Schwartzman

{"title":"An approximation to peak detection power using Gaussian random field theory","authors":"Yu Zhao , Dan Cheng , Armin Schwartzman","doi":"10.1016/j.jmva.2024.105346","DOIUrl":"10.1016/j.jmva.2024.105346","url":null,"abstract":"<div><p>We study power approximation formulas for peak detection using Gaussian random field theory. The approximation, based on the expected number of local maxima above the threshold <span><math><mi>u</mi></math></span>, <span><math><mrow><mi>E</mi><mrow><mo>[</mo><msub><mrow><mi>M</mi></mrow><mrow><mi>u</mi></mrow></msub><mo>]</mo></mrow></mrow></math></span>, is proved to work well under three asymptotic scenarios: small domain, large threshold, and sharp signal. An adjusted version of <span><math><mrow><mi>E</mi><mrow><mo>[</mo><msub><mrow><mi>M</mi></mrow><mrow><mi>u</mi></mrow></msub><mo>]</mo></mrow></mrow></math></span> is also proposed to improve accuracy when the expected number of local maxima <span><math><mrow><mi>E</mi><mrow><mo>[</mo><msub><mrow><mi>M</mi></mrow><mrow><mo>−</mo><mi>∞</mi></mrow></msub><mo>]</mo></mrow></mrow></math></span> exceeds 1. Cheng and Schwartzman (2018) developed explicit formulas for <span><math><mrow><mi>E</mi><mrow><mo>[</mo><msub><mrow><mi>M</mi></mrow><mrow><mi>u</mi></mrow></msub><mo>]</mo></mrow></mrow></math></span> of smooth isotropic Gaussian random fields with zero mean. In this paper, these formulas are extended to allow for rotational symmetric mean functions, making them applicable not only for power calculations but also for other areas of application that involve non-centered Gaussian random fields. We also apply our formulas to 2D and 3D simulated datasets, and the 3D data is induced by a group analysis of fMRI data from the Human Connectome Project to measure performance in a realistic setting.</p></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141871667","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

Double penalized variable selection for high-dimensional partial linear mixed effects models 高维偏线性混合效应模型的双惩罚变量选择

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2024-07-09 DOI: 10.1016/j.jmva.2024.105345

Yiping Yang, Chuanqin Luo, Weiming Yang

引用次数: 0

Stochastic hyperplane-based ranks and their use in multivariate portmanteau tests 基于随机超平面的等级及其在多元波特曼检验中的应用

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2024-06-28 DOI: 10.1016/j.jmva.2024.105344

Šárka Hudecová , Miroslav Šiman

引用次数: 0

Composite expectile estimation in partial functional linear regression model 偏函数线性回归模型中的复合期望值估计

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2024-06-19 DOI: 10.1016/j.jmva.2024.105343

Ping Yu , Xinyuan Song , Jiang Du

{"title":"Composite expectile estimation in partial functional linear regression model","authors":"Ping Yu , Xinyuan Song , Jiang Du","doi":"10.1016/j.jmva.2024.105343","DOIUrl":"https://doi.org/10.1016/j.jmva.2024.105343","url":null,"abstract":"<div><p>Recent research and substantive studies have shown growing interest in expectile regression (ER) procedures. Similar to quantile regression, ER with respect to different expectile levels can provide a comprehensive picture of the conditional distribution of a response variable given predictors. This study proposes three composite-type ER estimators to improve estimation accuracy. The proposed ER estimators include the composite estimator, which minimizes the composite expectile objective function across expectiles; the weighted expectile average estimator, which takes the weighted average of expectile-specific estimators; and the weighted composite estimator, which minimizes the weighted composite expectile objective function across expectiles. Under certain regularity conditions, we derive the convergence rate of the slope function, obtain the mean squared prediction error, and establish the asymptotic normality of the slope vector. Simulations are conducted to assess the empirical performances of various estimators. An application to the analysis of capital bike share data is presented. The numerical evidence endorses our theoretical results and confirm the superiority of the composite-type ER estimators to the conventional least squares and single ER estimators.</p></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141485003","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

Exponential bounds for regularized Hotelling’s T2 statistic in high dimension 正则化霍特林 Tn2 的指数边界</mml：

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2024-06-12 DOI: 10.1016/j.jmva.2024.105342

El Mehdi Issouani , Patrice Bertail , Emmanuelle Gautherat

引用次数: 0

Fisher’s pioneering work on discriminant analysis and its impact on Artificial Intelligence 费舍尔在判别分析方面的开创性工作及其对人工智能的影响

IF 1.6 3区数学

Journal of Multivariate Analysis Pub Date : 2024-06-10 DOI: 10.1016/j.jmva.2024.105341

Kanti V. Mardia

{"title":"Fisher’s pioneering work on discriminant analysis and its impact on Artificial Intelligence","authors":"Kanti V. Mardia","doi":"10.1016/j.jmva.2024.105341","DOIUrl":"10.1016/j.jmva.2024.105341","url":null,"abstract":"<div><p>Sir Ronald Aylmer Fisher opened many new areas in Multivariate Analysis, and the one which we will consider is discriminant analysis. Several papers by Fisher and others followed from his seminal paper in 1936 where he coined the name discrimination function. Historically, his four papers on discriminant analysis during 1936–1940 connect to the contemporaneous pioneering work of Hotelling and Mahalanobis. We revisit the famous iris data which Fisher used in his 1936 paper and in particular, test the hypothesis of multivariate normality for the data which he assumed. Fisher constructed his genetic discriminant motivated by this application and we provide a deeper insight into this construction; however, this construction has not been well understood as far as we know. We also indicate how the subject has developed along with the computer revolution, noting newer methods to carry out discriminant analysis, such as kernel classifiers, classification trees, support vector machines, neural networks, and deep learning. Overall, with computational power, the whole subject of Multivariate Analysis has changed its emphasis but the impact of this Fisher’s pioneering work continues as an integral part of supervised learning in Artificial Intelligence (AI).</p></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141403667","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

Max-convolution processes with random shape indicator kernels 具有随机形状指示核的最大卷积过程

IF 1.6 3区数学

Journal of Multivariate Analysis Pub Date : 2024-06-06 DOI: 10.1016/j.jmva.2024.105340

Pavel Krupskii , Raphaël Huser

{"title":"Max-convolution processes with random shape indicator kernels","authors":"Pavel Krupskii , Raphaël Huser","doi":"10.1016/j.jmva.2024.105340","DOIUrl":"https://doi.org/10.1016/j.jmva.2024.105340","url":null,"abstract":"<div><p>In this paper, we introduce a new class of models for spatial data obtained from max-convolution processes based on indicator kernels with random shape. We show that these models have appealing dependence properties including tail dependence at short distances and independence at long distances. We further consider max-convolutions between such processes and processes with tail independence, in order to separately control the bulk and tail dependence behaviors, and to increase flexibility of the model at longer distances, in particular, to capture intermediate tail dependence. We show how parameters can be estimated using a weighted pairwise likelihood approach, and we conduct an extensive simulation study to show that the proposed inference approach is feasible in relatively high dimensions and it yields accurate parameter estimates in most cases. We apply the proposed methodology to analyze daily temperature maxima measured at 100 monitoring stations in the state of Oklahoma, US. Our results indicate that our proposed model provides a good fit to the data, and that it captures both the bulk and the tail dependence structures accurately.</p></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0047259X24000472/pdfft?md5=6e148f4b405bc0c38b2fef0ced10dc6b&pid=1-s2.0-S0047259X24000472-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141313857","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

Low-rank tensor regression for selection of grouped variables 用于选择分组变量的低秩张量回归

IF 1.6 3区数学

Journal of Multivariate Analysis Pub Date : 2024-06-05 DOI: 10.1016/j.jmva.2024.105339

Yang Chen, Ziyan Luo, Lingchen Kong

引用次数: 0

Bias correction for kernel density estimation with spherical data 球形数据核密度估计的偏差修正

IF 1.6 3区数学

Journal of Multivariate Analysis Pub Date : 2024-06-01 DOI: 10.1016/j.jmva.2024.105338

Yasuhito Tsuruta

引用次数: 0