{"title":"心理方差分量模型的广义脊估计","authors":"W. Haiying, Tong Hengqing, Li Tianzhen","doi":"10.1109/ICBBE.2008.642","DOIUrl":null,"url":null,"abstract":"The variance component (VC) model is popular for psychology analysis. In this paper we generalize the method of ridge estimate into variance component model by means of the correspondence between deviation and mean. Then we prove that the mean square error of the ridge estimation is less than those of the common estimation. At last we give reasonable selections of the ridge parameters.","PeriodicalId":6399,"journal":{"name":"2008 2nd International Conference on Bioinformatics and Biomedical Engineering","volume":"1 1","pages":"1256-1259"},"PeriodicalIF":0.0000,"publicationDate":"2008-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Generalized Ridge Estimation for Psychology Variance Component Model\",\"authors\":\"W. Haiying, Tong Hengqing, Li Tianzhen\",\"doi\":\"10.1109/ICBBE.2008.642\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The variance component (VC) model is popular for psychology analysis. In this paper we generalize the method of ridge estimate into variance component model by means of the correspondence between deviation and mean. Then we prove that the mean square error of the ridge estimation is less than those of the common estimation. At last we give reasonable selections of the ridge parameters.\",\"PeriodicalId\":6399,\"journal\":{\"name\":\"2008 2nd International Conference on Bioinformatics and Biomedical Engineering\",\"volume\":\"1 1\",\"pages\":\"1256-1259\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-05-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 2nd International Conference on Bioinformatics and Biomedical Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICBBE.2008.642\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 2nd International Conference on Bioinformatics and Biomedical Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICBBE.2008.642","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Generalized Ridge Estimation for Psychology Variance Component Model
The variance component (VC) model is popular for psychology analysis. In this paper we generalize the method of ridge estimate into variance component model by means of the correspondence between deviation and mean. Then we prove that the mean square error of the ridge estimation is less than those of the common estimation. At last we give reasonable selections of the ridge parameters.
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