{"title":"基于Kullback-Leibler散度距离的模型置信集","authors":"G. Barmalzan, T. P. Najafabad","doi":"10.18869/ACADPUB.JSRI.9.2.179","DOIUrl":null,"url":null,"abstract":"Consider the problem of estimating true density, h(·) based upon a random sample X1, . . . , Xn. In general, h(·) is approximated using an appropriate (in some sense, see below) model fθ(x). This article using Vuong’s (1989) test along with a collection of k(> 2) non-nested models constructs a set of appropriate models, say model confidence set, for unknown model h(·). Application of such confidence set has been confirmed through a simulation study.","PeriodicalId":422124,"journal":{"name":"Journal of Statistical Research of Iran","volume":"99 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Model Confidence Set Based on Kullback-Leibler Divergence Distance\",\"authors\":\"G. Barmalzan, T. P. Najafabad\",\"doi\":\"10.18869/ACADPUB.JSRI.9.2.179\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Consider the problem of estimating true density, h(·) based upon a random sample X1, . . . , Xn. In general, h(·) is approximated using an appropriate (in some sense, see below) model fθ(x). This article using Vuong’s (1989) test along with a collection of k(> 2) non-nested models constructs a set of appropriate models, say model confidence set, for unknown model h(·). Application of such confidence set has been confirmed through a simulation study.\",\"PeriodicalId\":422124,\"journal\":{\"name\":\"Journal of Statistical Research of Iran\",\"volume\":\"99 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-03-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Statistical Research of Iran\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.18869/ACADPUB.JSRI.9.2.179\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Statistical Research of Iran","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18869/ACADPUB.JSRI.9.2.179","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Model Confidence Set Based on Kullback-Leibler Divergence Distance
Consider the problem of estimating true density, h(·) based upon a random sample X1, . . . , Xn. In general, h(·) is approximated using an appropriate (in some sense, see below) model fθ(x). This article using Vuong’s (1989) test along with a collection of k(> 2) non-nested models constructs a set of appropriate models, say model confidence set, for unknown model h(·). Application of such confidence set has been confirmed through a simulation study.
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