Journal of Multivariate Analysis最新文献_第5页

Birge ratio method for modeling dark uncertainty in multivariate meta-analyses and inter-laboratory studies 用于多元荟萃分析和实验室间研究中暗不确定性建模的比尔吉比率法

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2024-10-05 DOI: 10.1016/j.jmva.2024.105376

Olha Bodnar , Taras Bodnar

引用次数: 0

Explicit bivariate simplicial depth 显式二维简单深度

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2024-09-30 DOI: 10.1016/j.jmva.2024.105375

Erik Mendroš, Stanislav Nagy

引用次数: 0

Large sample correlation matrices with unbounded spectrum 具有无界频谱的大样本相关矩阵

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2024-09-27 DOI: 10.1016/j.jmva.2024.105373

Yanpeng Li

引用次数: 0

Detection and localization of changes in a panel of densities 密度面板变化的检测和定位

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2024-09-24 DOI: 10.1016/j.jmva.2024.105374

Tim Kutta , Agnieszka Jach , Michel Ferreira Cardia Haddad , Piotr Kokoszka , Haonan Wang

{"title":"Detection and localization of changes in a panel of densities","authors":"Tim Kutta , Agnieszka Jach , Michel Ferreira Cardia Haddad , Piotr Kokoszka , Haonan Wang","doi":"10.1016/j.jmva.2024.105374","DOIUrl":"10.1016/j.jmva.2024.105374","url":null,"abstract":"<div><div>We propose a new methodology for identifying and localizing changes in the Fréchet mean of a multivariate time series of probability densities. The functional data objects we study are random densities <span><math><msub><mrow><mi>f</mi></mrow><mrow><mi>s</mi><mo>,</mo><mi>t</mi></mrow></msub></math></span> indexed by discrete time <span><math><mi>t</mi></math></span> and a vector component <span><math><mi>s</mi></math></span>, which can be treated as a broadly understood spatial location. Our main objective is to identify the set of components <span><math><mi>s</mi></math></span>, where a change occurs with statistical certainty. A challenge of this analysis is that the densities <span><math><msub><mrow><mi>f</mi></mrow><mrow><mi>s</mi><mo>,</mo><mi>t</mi></mrow></msub></math></span> are not directly observable and must be estimated from sparse and potentially imbalanced data. Such setups are motivated by the analysis of two data sets that we investigate in this work. First, a hitherto unpublished large data set of Brazilian Covid infections and a second, a financial data set derived from intraday prices of U.S. Exchange Traded Funds. Chief statistical advances are the development of change point tests and estimators of components of change for multivariate time series of densities. We prove the theoretical validity of our methodology and investigate its finite sample performance in a simulation study.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"205 ","pages":"Article 105374"},"PeriodicalIF":1.4,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142327131","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

Data depth functions for non-standard data by use of formal concept analysis 利用形式概念分析实现非标准数据的数据深度函数

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2024-09-19 DOI: 10.1016/j.jmva.2024.105372

Hannah Blocher, Georg Schollmeyer

{"title":"Data depth functions for non-standard data by use of formal concept analysis","authors":"Hannah Blocher, Georg Schollmeyer","doi":"10.1016/j.jmva.2024.105372","DOIUrl":"10.1016/j.jmva.2024.105372","url":null,"abstract":"<div><div>In this article we introduce a notion of depth functions for data types that are not given in standard statistical data formats. We focus on data that cannot be represented by one specific data structure, such as normed vector spaces. This covers a wide range of different data types, which we refer to as non-standard data. Depth functions have been studied intensively for normed vector spaces. However, a discussion of depth functions for non-standard data is lacking. In this article, we address this gap by using formal concept analysis to obtain a unified data representation. Building on this representation, we then define depth functions for non-standard data. Furthermore, we provide a systematic basis by introducing structural properties using the data representation provided by formal concept analysis. Finally, we embed the generalised Tukey depth into our concept of data depth and analyse it using the introduced structural properties. Thus, this article presents the mathematical formalisation of centrality and outlyingness for non-standard data and increases the number of spaces in which centrality can be discussed. In particular, we provide a basis for defining further depth functions and statistical inference methods for non-standard data.</div></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"205 ","pages":"Article 105372"},"PeriodicalIF":1.4,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142327125","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

Scaled envelope models for multivariate time series 多变量时间序列的标度包络模型

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2024-09-13 DOI: 10.1016/j.jmva.2024.105370

H.M. Wiranthe B. Herath , S. Yaser Samadi

{"title":"Scaled envelope models for multivariate time series","authors":"H.M. Wiranthe B. Herath , S. Yaser Samadi","doi":"10.1016/j.jmva.2024.105370","DOIUrl":"10.1016/j.jmva.2024.105370","url":null,"abstract":"<div><p>Vector autoregressive (VAR) models have become a popular choice for modeling multivariate time series data due to their simplicity and ease of use. Efficient estimation of VAR coefficients is an important problem. The envelope technique for VAR models is demonstrated to have the potential to yield significant gains in efficiency and accuracy by incorporating linear combinations of the response vector that are essentially immaterial to the estimation of the VAR coefficients. However, inferences based on envelope VAR (EVAR) models are not invariant or equivariant upon the rescaling of the VAR responses, limiting their application to time series data that are measured in the same or similar units. In scenarios where VAR responses are measured on different scales, the efficiency improvements promised by envelopes are not always guaranteed. To address this limitation, we introduce the scaled envelope VAR (SEVAR) model, which preserves the efficiency-boosting capabilities of standard envelope techniques while remaining invariant to scale changes. The asymptotic characteristics of the proposed estimators are established based on different error assumptions. Simulation studies and real-data analysis are conducted to demonstrate the efficiency and effectiveness of the proposed model. The numerical results corroborate our theoretical findings.</p></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"205 ","pages":"Article 105370"},"PeriodicalIF":1.4,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0047259X24000770/pdfft?md5=bcb10a9c98d350b55789c52bc615d145&pid=1-s2.0-S0047259X24000770-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142240404","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

A bias-corrected Srivastava-type test for cross-sectional independence 经偏差校正的斯里瓦斯塔瓦式横截面独立性检验

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2024-09-13 DOI: 10.1016/j.jmva.2024.105371

Kai Xu , Mingxiang Cao , Qing Cheng

引用次数: 0

Invariant correlation under marginal transforms 边际变换下的不变相关性

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2024-08-30 DOI: 10.1016/j.jmva.2024.105361

Takaaki Koike , Liyuan Lin , Ruodu Wang

{"title":"Invariant correlation under marginal transforms","authors":"Takaaki Koike , Liyuan Lin , Ruodu Wang","doi":"10.1016/j.jmva.2024.105361","DOIUrl":"10.1016/j.jmva.2024.105361","url":null,"abstract":"<div><p>A useful property of independent samples is that their correlation remains the same after applying marginal transforms. This invariance property plays a fundamental role in statistical inference, but does not hold in general for dependent samples. In this paper, we study this invariance property on the Pearson correlation coefficient and its applications. A multivariate random vector is said to have an invariant correlation if its pairwise correlation coefficients remain unchanged under any common marginal transforms. For a bivariate case, we characterize all models of such a random vector via a certain combination of comonotonicity—the strongest form of positive dependence—and independence. In particular, we show that the class of exchangeable copulas with invariant correlation is precisely described by what we call positive Fréchet copulas. In the general multivariate case, we characterize the set of all invariant correlation matrices via the clique partition polytope. We also propose a positive regression dependent model that admits any prescribed invariant correlation matrix. Finally, we show that all our characterization results of invariant correlation, except one special case, remain the same if the common marginal transforms are confined to the set of increasing ones.</p></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"204 ","pages":"Article 105361"},"PeriodicalIF":1.4,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0047259X2400068X/pdfft?md5=87348d6db627c38f7dec7cb4cd435464&pid=1-s2.0-S0047259X2400068X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142122196","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

Grouped feature screening for ultrahigh-dimensional classification via Gini distance correlation 通过基尼距离相关性对超高维分类进行分组特征筛选

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2024-08-17 DOI: 10.1016/j.jmva.2024.105360

Yongli Sang , Xin Dang

引用次数: 0

The inner partial least square: An exploration of the “necessary” dimension reduction 内部分最小平方：对 "必要 "降维的探索

IF 1.4 3区数学

Journal of Multivariate Analysis Pub Date : 2024-08-14 DOI: 10.1016/j.jmva.2024.105356

Yunjian Yin, Lan Liu

{"title":"The inner partial least square: An exploration of the “necessary” dimension reduction","authors":"Yunjian Yin, Lan Liu","doi":"10.1016/j.jmva.2024.105356","DOIUrl":"10.1016/j.jmva.2024.105356","url":null,"abstract":"<div><p>The partial least square (PLS) algorithm retains the combinations of predictors that maximize the covariance with the outcome. Cook et al. (2013) showed that PLS results in a predictor envelope, which is the smallest reducing subspace of predictors’ covariance that contains the coefficient. However, PLS and predictor envelope both target at a space that contains the regression coefficients and therefore they may sometimes be too conservative to reduce the dimension of the predictors. In this paper, we propose a new method that may improve the estimation efficiency of regression coefficients when both PLS and predictor envelope fail to do so. Specifically, our method results in the largest reducing subspace of predictors’ covariance that is contained in the coefficient matrix space. Interestingly, the moment based algorithm of our proposed method can be achieved by changing the max in PLS to min. We define the modified PLS as the inner PLS and the resulting space as the inner predictor envelope space. We provide the theoretical properties of our proposed methods as well as demonstrate their use in China Health and Nutrition Survey.</p></div>","PeriodicalId":16431,"journal":{"name":"Journal of Multivariate Analysis","volume":"204 ","pages":"Article 105356"},"PeriodicalIF":1.4,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142083637","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