多元混合数据的随机效应模型:基于parafac的有限混合方法

IF 1.2 4区数学 Q2 STATISTICS & PROBABILITY

Statistical Modelling Pub Date : 2021-09-01 DOI:10.1177/1471082X211037405

M. Alfò, P. Giordani

{"title":"多元混合数据的随机效应模型:基于parafac的有限混合方法","authors":"M. Alfò, P. Giordani","doi":"10.1177/1471082X211037405","DOIUrl":null,"url":null,"abstract":"We discuss a flexible regression model for multivariate mixed responses. Dependence between outcomes is introduced via the joint distribution of discrete outcome- and individual-specific random effects that represent potential unobserved heterogeneity in each outcome profile. A different number of locations can be used for each margin, and the association structure is described by a tensor that can be further simplified by using the Parafac model. A case study illustrates the proposal.","PeriodicalId":49476,"journal":{"name":"Statistical Modelling","volume":"22 1","pages":"46 - 66"},"PeriodicalIF":1.2000,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Random effect models for multivariate mixed data: A Parafac-based finite mixture approach\",\"authors\":\"M. Alfò, P. Giordani\",\"doi\":\"10.1177/1471082X211037405\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We discuss a flexible regression model for multivariate mixed responses. Dependence between outcomes is introduced via the joint distribution of discrete outcome- and individual-specific random effects that represent potential unobserved heterogeneity in each outcome profile. A different number of locations can be used for each margin, and the association structure is described by a tensor that can be further simplified by using the Parafac model. A case study illustrates the proposal.\",\"PeriodicalId\":49476,\"journal\":{\"name\":\"Statistical Modelling\",\"volume\":\"22 1\",\"pages\":\"46 - 66\"},\"PeriodicalIF\":1.2000,\"publicationDate\":\"2021-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Statistical Modelling\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://doi.org/10.1177/1471082X211037405\",\"RegionNum\":4,\"RegionCategory\":\"数学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"STATISTICS & PROBABILITY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Statistical Modelling","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1177/1471082X211037405","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}

引用次数: 0

摘要

我们讨论了多元混合反应的一个灵活回归模型。结果之间的依赖性是通过离散结果和个体特定随机效应的联合分布引入的，这些效应代表了每个结果剖面中潜在的未观察到的异质性。每个边缘可以使用不同数量的位置，并且关联结构由张量描述，该张量可以通过使用Parafac模型进一步简化。一个案例研究说明了这一建议。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Random effect models for multivariate mixed data: A Parafac-based finite mixture approach

We discuss a flexible regression model for multivariate mixed responses. Dependence between outcomes is introduced via the joint distribution of discrete outcome- and individual-specific random effects that represent potential unobserved heterogeneity in each outcome profile. A different number of locations can be used for each margin, and the association structure is described by a tensor that can be further simplified by using the Parafac model. A case study illustrates the proposal.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Statistical Modelling 数学-统计学与概率论

CiteScore

2.20

自引率

0.00%

发文量

审稿时长

>12 weeks

期刊介绍： The primary aim of the journal is to publish original and high-quality articles that recognize statistical modelling as the general framework for the application of statistical ideas. Submissions must reflect important developments, extensions, and applications in statistical modelling. The journal also encourages submissions that describe scientifically interesting, complex or novel statistical modelling aspects from a wide diversity of disciplines, and submissions that embrace the diversity of applied statistical modelling.