Asta-Advances in Statistical Analysis最新文献

Sparse group matrix regression model and its application in stock movements prediction 稀疏群矩阵回归模型及其在股票走势预测中的应用

IF 1.6 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2026-08-07 DOI: 10.1007/s10182-026-00568-3

Bingzhen Chen, Wenjuan Zhai

引用次数: 0

Bayesian estimation approach for linear regression models with linear inequality restrictions 具有线性不等式约束的线性回归模型的贝叶斯估计方法

IF 1.6 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2026-06-12 DOI: 10.1007/s10182-026-00564-7

Solmaz Seifollahi, Kaniav Kamary, Hossein Bevrani

引用次数: 0

A dyadic multivariate mixture model for ordinal responses: application to coach–athlete interactions 有序响应的二元多元混合模型：在教练员-运动员相互作用中的应用

IF 1.6 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2026-05-20 DOI: 10.1007/s10182-026-00563-8

Maria Iannario, Dimitris Karlis

{"title":"A dyadic multivariate mixture model for ordinal responses: application to coach–athlete interactions","authors":"Maria Iannario, Dimitris Karlis","doi":"10.1007/s10182-026-00563-8","DOIUrl":"10.1007/s10182-026-00563-8","url":null,"abstract":"<div><p>Dyadic data analysis is increasingly used to model intra- and inter-personal mechanisms across disciplines such as psychology, education, healthcare, and the social sciences. Examples of dyads include husband–wife, doctor–patient, parent–child, and coach–athlete relationships. Such data typically violate the standard independence assumption due to the inherent interdependence between dyad members, requiring more sophisticated modeling approaches. In this paper, we introduce a novel statistical framework—the Dyadic Multivariate Mixture Model (DMMM)—designed to address these complexities in the context of ordinal multivariate responses. The DMMM integrates techniques from dyadic analysis, finite mixtures, copula-based dependence modeling, and ordinal regression, offering a flexible and parsimonious way to capture latent interpersonal traits and heterogeneous patterns of interaction. While the motivating application concerns coach–athlete relationships in competitive swimming—where both members of the dyad responded to a series of Likert-scale items—the model itself is general and applicable across a wide range of domains where dyadic ordinal data are observed. These include clinical psychology (e.g., therapist–client), organizational settings (e.g., supervisor–employee), and educational contexts (e.g., teacher–student). The proposed methodology allows for the identification of latent dyadic profiles, the modeling of interdependence between responses, and the inclusion of covariates at multiple levels. A case study involving elite swimmers demonstrates the model’s ability to address three key research questions: measuring the strength of relational ties, identifying group differences in communication dynamics, and evaluating personality-related covariates that shape latent relational patterns.</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":"110 3","pages":"585 - 611"},"PeriodicalIF":1.6,"publicationDate":"2026-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10182-026-00563-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148807231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

An iterative plug-in algorithm for realized kernels 实现核的迭代插件算法

IF 1.6 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2026-05-14 DOI: 10.1007/s10182-026-00562-9

Yuanhua Feng, Chen Zhou

引用次数: 0

Generalized information criteria for high-dimensional sparse statistical jump models 高维稀疏统计跳跃模型的广义信息准则

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2026-04-03 DOI: 10.1007/s10182-026-00554-9

Federico P. Cortese, Petter N. Kolm, Erik Lindström

引用次数: 0

A two-stage approach for modeling non-sparse main effects with sparse interaction terms 用稀疏交互项建模非稀疏主效应的两阶段方法

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2026-03-02 DOI: 10.1007/s10182-026-00555-8

Shun Yu, Yujie Gai, Yuehan Yang

引用次数: 0

Bayesian dynamic panel-ordered probit model with individual heterogeneity 具有个体异质性的贝叶斯动态面板有序概率模型

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2026-02-11 DOI: 10.1007/s10182-026-00553-w

Lei Shi, Yixin Zhang

引用次数: 0

A note on dynamic spatiotemporal ARCH models: small- and large-sample results 动态时空ARCH模型注释：小样本和大样本结果

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2025-12-19 DOI: 10.1007/s10182-025-00552-3

Philipp Otto, Osman Doğan, Süleyman Taşpınar

引用次数: 0

Small area estimation of poverty indicators under bivariate Fay–Herriot model with correlated time effects 具有相关时间效应的二元Fay-Herriot模型下贫困指标的小面积估计

IF 1.6 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2025-12-19 DOI: 10.1007/s10182-025-00550-5

Esteban Cabello, María Dolores Esteban, Domingo Morales, Agustín Pérez

引用次数: 0

General multiple tests for functional data 功能数据的一般多重测试

IF 1.6 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2025-12-15 DOI: 10.1007/s10182-025-00549-y

Merle Munko, Marc Ditzhaus, Markus Pauly, Jin-Ting Zhang, Łukasz Smaga

{"title":"General multiple tests for functional data","authors":"Merle Munko, Marc Ditzhaus, Markus Pauly, Jin-Ting Zhang, Łukasz Smaga","doi":"10.1007/s10182-025-00549-y","DOIUrl":"10.1007/s10182-025-00549-y","url":null,"abstract":"<div><p>While there exists several inferential methods for analyzing functional data in factorial designs, there is a lack of statistical tests that are valid (i) in general designs, (ii) under non-restrictive assumptions on the data generating process and (iii) allow for coherent post hoc analyses. In particular, most existing methods assume Gaussianity or equal covariance functions across groups (homoscedasticity) and are only applicable for specific study designs that do not allow for evaluation of interactions. Moreover, almost all strategies published to date are aimed at testing global hypotheses and do not directly allow a more in-depth analysis of multiple local hypotheses. To address the first two problems (i-ii), we propose flexible integral-type test statistics that are applicable in general factorial designs under minimal assumptions on the data generating process. In particular, we neither postulate homoscedasticity nor Gaussianity. To approximate the statistics’ null distribution, we adopt a resampling approach and validate it methodologically. Finally, we use our flexible testing framework to (iii) infer several local null hypotheses simultaneously. To allow for powerful data analysis, we thereby take the complex dependencies of the different local test statistics into account. In extensive simulations we confirm that the new methods are flexibly applicable. Two illustrative data analyses complete our study. The new testing procedures are implemented in the R package multiFANOVA, which is available on CRAN.</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":"110 3","pages":"489 - 518"},"PeriodicalIF":1.6,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10182-025-00549-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148807487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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