复杂调查抽样下二元因素分析模型的成对似然估计和有限信息拟合优度检验统计。

IF 1.5 3区心理学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

British Journal of Mathematical & Statistical Psychology Pub Date : 2024-10-12 DOI:10.1111/bmsp.12358

Haziq Jamil, Irini Moustaki, Chris Skinner

{"title":"复杂调查抽样下二元因素分析模型的成对似然估计和有限信息拟合优度检验统计。","authors":"Haziq Jamil, Irini Moustaki, Chris Skinner","doi":"10.1111/bmsp.12358","DOIUrl":null,"url":null,"abstract":"This paper discusses estimation and limited-information goodness-of-fit test statistics in factor models for binary data using pairwise likelihood estimation and sampling weights. The paper extends the applicability of pairwise likelihood estimation for factor models with binary data to accommodate complex sampling designs. Additionally, it introduces two key limited-information test statistics: the Pearson chi-squared test and the Wald test. To enhance computational efficiency, the paper introduces modifications to both test statistics. The performance of the estimation and the proposed test statistics under simple random sampling and unequal probability sampling is evaluated using simulated data.","PeriodicalId":55322,"journal":{"name":"British Journal of Mathematical & Statistical Psychology","volume":" ","pages":""},"PeriodicalIF":1.5000,"publicationDate":"2024-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Pairwise likelihood estimation and limited-information goodness-of-fit test statistics for binary factor analysis models under complex survey sampling.\",\"authors\":\"Haziq Jamil, Irini Moustaki, Chris Skinner\",\"doi\":\"10.1111/bmsp.12358\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper discusses estimation and limited-information goodness-of-fit test statistics in factor models for binary data using pairwise likelihood estimation and sampling weights. The paper extends the applicability of pairwise likelihood estimation for factor models with binary data to accommodate complex sampling designs. Additionally, it introduces two key limited-information test statistics: the Pearson chi-squared test and the Wald test. To enhance computational efficiency, the paper introduces modifications to both test statistics. The performance of the estimation and the proposed test statistics under simple random sampling and unequal probability sampling is evaluated using simulated data.\",\"PeriodicalId\":55322,\"journal\":{\"name\":\"British Journal of Mathematical & Statistical Psychology\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":1.5000,\"publicationDate\":\"2024-10-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"British Journal of Mathematical & Statistical Psychology\",\"FirstCategoryId\":\"102\",\"ListUrlMain\":\"https://doi.org/10.1111/bmsp.12358\",\"RegionNum\":3,\"RegionCategory\":\"心理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"British Journal of Mathematical & Statistical Psychology","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.1111/bmsp.12358","RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}

引用次数: 0

摘要

本文讨论了使用成对似然估计和抽样权重对二元数据的因子模型进行估计和有限信息拟合优度检验统计。本文扩展了成对似然估计对二元数据因子模型的适用性，以适应复杂的抽样设计。此外，论文还介绍了两个关键的有限信息检验统计量：皮尔逊卡方检验和沃尔德检验。为了提高计算效率，本文对这两个检验统计量进行了修改。本文使用模拟数据评估了在简单随机抽样和不等概率抽样条件下的估计和所提出的检验统计量的性能。

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

Pairwise likelihood estimation and limited-information goodness-of-fit test statistics for binary factor analysis models under complex survey sampling.

This paper discusses estimation and limited-information goodness-of-fit test statistics in factor models for binary data using pairwise likelihood estimation and sampling weights. The paper extends the applicability of pairwise likelihood estimation for factor models with binary data to accommodate complex sampling designs. Additionally, it introduces two key limited-information test statistics: the Pearson chi-squared test and the Wald test. To enhance computational efficiency, the paper introduces modifications to both test statistics. The performance of the estimation and the proposed test statistics under simple random sampling and unequal probability sampling is evaluated using simulated data.

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

British Journal of Mathematical & Statistical Psychology 医学-数学跨学科应用

CiteScore

5.00

自引率

3.80%

发文量

审稿时长

>12 weeks

期刊介绍： The British Journal of Mathematical and Statistical Psychology publishes articles relating to areas of psychology which have a greater mathematical or statistical aspect of their argument than is usually acceptable to other journals including: • mathematical psychology • statistics • psychometrics • decision making • psychophysics • classification • relevant areas of mathematics, computing and computer software These include articles that address substantitive psychological issues or that develop and extend techniques useful to psychologists. New models for psychological processes, new approaches to existing data, critiques of existing models and improved algorithms for estimating the parameters of a model are examples of articles which may be favoured.