{"title":"超高维广义线性混合模型中的线性假设检验","authors":"Xiyun Zhang, Zaixing Li","doi":"10.1007/s42952-024-00268-1","DOIUrl":null,"url":null,"abstract":"<p>This paper is concerned with linear hypothesis testing problems in ultra high dimensional generalized linear mixed models where the response and the random effects are distribution-free. The constrained-partial-regularization based penalized quasi-likelihood method is proposed and the corresponding statistical properties are studied. To test linear hypotheses, we propose a partial penalized quasi-likelihood ratio test, a partial penalized quasi-score test, and a partial penalized Wald test. The theoretical properties of these three tests are established under both the null and the alternatives. The finite sample performance of the proposed tests has been shown by the simulation studies, and the forest health data is illustrated by our procedure.</p>","PeriodicalId":49992,"journal":{"name":"Journal of the Korean Statistical Society","volume":"21 1","pages":""},"PeriodicalIF":0.6000,"publicationDate":"2024-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Linear hypothesis testing in ultra high dimensional generalized linear mixed models\",\"authors\":\"Xiyun Zhang, Zaixing Li\",\"doi\":\"10.1007/s42952-024-00268-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>This paper is concerned with linear hypothesis testing problems in ultra high dimensional generalized linear mixed models where the response and the random effects are distribution-free. The constrained-partial-regularization based penalized quasi-likelihood method is proposed and the corresponding statistical properties are studied. To test linear hypotheses, we propose a partial penalized quasi-likelihood ratio test, a partial penalized quasi-score test, and a partial penalized Wald test. The theoretical properties of these three tests are established under both the null and the alternatives. The finite sample performance of the proposed tests has been shown by the simulation studies, and the forest health data is illustrated by our procedure.</p>\",\"PeriodicalId\":49992,\"journal\":{\"name\":\"Journal of the Korean Statistical Society\",\"volume\":\"21 1\",\"pages\":\"\"},\"PeriodicalIF\":0.6000,\"publicationDate\":\"2024-05-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of the Korean Statistical Society\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://doi.org/10.1007/s42952-024-00268-1\",\"RegionNum\":4,\"RegionCategory\":\"数学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"STATISTICS & PROBABILITY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of the Korean Statistical Society","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1007/s42952-024-00268-1","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
Linear hypothesis testing in ultra high dimensional generalized linear mixed models
This paper is concerned with linear hypothesis testing problems in ultra high dimensional generalized linear mixed models where the response and the random effects are distribution-free. The constrained-partial-regularization based penalized quasi-likelihood method is proposed and the corresponding statistical properties are studied. To test linear hypotheses, we propose a partial penalized quasi-likelihood ratio test, a partial penalized quasi-score test, and a partial penalized Wald test. The theoretical properties of these three tests are established under both the null and the alternatives. The finite sample performance of the proposed tests has been shown by the simulation studies, and the forest health data is illustrated by our procedure.
期刊介绍:
The Journal of the Korean Statistical Society publishes research articles that make original contributions to the theory and methodology of statistics and probability. It also welcomes papers on innovative applications of statistical methodology, as well as papers that give an overview of current topic of statistical research with judgements about promising directions for future work. The journal welcomes contributions from all countries.