贝叶斯验证性因子分析在行为测量中的应用:贝叶斯参数估计的强收敛性

IF 0.6 Q3 SOCIAL SCIENCES, INTERDISCIPLINARY Measurement-Interdisciplinary Research and Perspectives Pub Date : 2022-10-02 DOI:10.1080/15366367.2021.2005959
T. Raykov, Philipp Doebler, G. Marcoulides
{"title":"贝叶斯验证性因子分析在行为测量中的应用:贝叶斯参数估计的强收敛性","authors":"T. Raykov, Philipp Doebler, G. Marcoulides","doi":"10.1080/15366367.2021.2005959","DOIUrl":null,"url":null,"abstract":"ABSTRACT This article is concerned with the large-sample parameter estimatorbehavior in applications of Bayesian confirmatory factor analysis in behavioral measurement. The property of strong convergence of the popular Bayesian posterior median estimator is discussed, which states numerical convergence with probability 1 of the resulting estimates to the population parameter value as sample size increases without bound. This property is stronger than the consistency and convergence in distribution of that estimator, which have been commonly referred to in the literature. A numerical example is utilized to illustrate this almost sure convergence of a Bayesian latent correlation estimator. The paper contributes to the body of research on optimal statistical features of Bayesian estimates and concludes with a discussion of the implications of this large-sample property of the Bayesian median estimator for empirical measurement studies.","PeriodicalId":46596,"journal":{"name":"Measurement-Interdisciplinary Research and Perspectives","volume":"29 1","pages":"215 - 227"},"PeriodicalIF":0.6000,"publicationDate":"2022-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Applications of Bayesian Confirmatory Factor Analysis in Behavioral Measurement: Strong Convergence of a Bayesian Parameter Estimator\",\"authors\":\"T. Raykov, Philipp Doebler, G. Marcoulides\",\"doi\":\"10.1080/15366367.2021.2005959\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"ABSTRACT This article is concerned with the large-sample parameter estimatorbehavior in applications of Bayesian confirmatory factor analysis in behavioral measurement. The property of strong convergence of the popular Bayesian posterior median estimator is discussed, which states numerical convergence with probability 1 of the resulting estimates to the population parameter value as sample size increases without bound. This property is stronger than the consistency and convergence in distribution of that estimator, which have been commonly referred to in the literature. A numerical example is utilized to illustrate this almost sure convergence of a Bayesian latent correlation estimator. The paper contributes to the body of research on optimal statistical features of Bayesian estimates and concludes with a discussion of the implications of this large-sample property of the Bayesian median estimator for empirical measurement studies.\",\"PeriodicalId\":46596,\"journal\":{\"name\":\"Measurement-Interdisciplinary Research and Perspectives\",\"volume\":\"29 1\",\"pages\":\"215 - 227\"},\"PeriodicalIF\":0.6000,\"publicationDate\":\"2022-10-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Measurement-Interdisciplinary Research and Perspectives\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/15366367.2021.2005959\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"SOCIAL SCIENCES, INTERDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Measurement-Interdisciplinary Research and Perspectives","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/15366367.2021.2005959","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"SOCIAL SCIENCES, INTERDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

摘要

本文研究了贝叶斯验证性因子分析在行为测量中的应用。讨论了常用的贝叶斯后验中值估计器的强收敛性,该估计器对总体参数值的估计概率为1,当样本容量无界增加时。这一性质比文献中通常提到的估计量在分布上的相合性和收敛性更强。用一个数值例子说明了贝叶斯隐相关估计的这种几乎肯定的收敛性。本文对贝叶斯估计的最优统计特征的研究做出了贡献,并以贝叶斯中值估计的这种大样本性质对实证测量研究的影响进行了讨论。
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
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Applications of Bayesian Confirmatory Factor Analysis in Behavioral Measurement: Strong Convergence of a Bayesian Parameter Estimator
ABSTRACT This article is concerned with the large-sample parameter estimatorbehavior in applications of Bayesian confirmatory factor analysis in behavioral measurement. The property of strong convergence of the popular Bayesian posterior median estimator is discussed, which states numerical convergence with probability 1 of the resulting estimates to the population parameter value as sample size increases without bound. This property is stronger than the consistency and convergence in distribution of that estimator, which have been commonly referred to in the literature. A numerical example is utilized to illustrate this almost sure convergence of a Bayesian latent correlation estimator. The paper contributes to the body of research on optimal statistical features of Bayesian estimates and concludes with a discussion of the implications of this large-sample property of the Bayesian median estimator for empirical measurement studies.
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
Measurement-Interdisciplinary Research and Perspectives
Measurement-Interdisciplinary Research and Perspectives SOCIAL SCIENCES, INTERDISCIPLINARY-
CiteScore
1.80
自引率
0.00%
发文量
23
期刊最新文献
A Latent Trait Approach to the Measurement of Physical Fitness Application of Machine Learning Techniques for Fake News Classification The Use of Multidimensional Item Response Theory Estimations in Controlling Differential Item Functioning Opinion Instability and Measurement Errors: A G-Theory Analysis of College Students Predicting the Risk of Diabetes and Heart Disease with Machine Learning Classifiers: The Mediation Analysis
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1