{"title":"巴特利特似然比统计量的调整及最大似然估计量的分布","authors":"O. Barndorff-Nielsen, D. Cox","doi":"10.1111/J.2517-6161.1984.TB01321.X","DOIUrl":null,"url":null,"abstract":"For rather general parametric models, a simple connection is established between the Bartlett adjustment factor of the log-likelihood ratio statistic and the normalizing constant c of the formula c I I 1?2L for the conditional distribution of a maximum likelihood estimator as applied to the full model and the model of the hypothesis tested. This leads to a relatively simple demonstration that division of the likelihood ratio statistic by a suitable constant or estimated factor improves the chi-squared approximation to its distribution. Various expressions for these quantities are discussed. In particular, for the case of a one-dimensional parameter an approximation to the constants involved is derived, which does not require integration over the sample space.","PeriodicalId":17425,"journal":{"name":"Journal of the royal statistical society series b-methodological","volume":"27 4","pages":"483-495"},"PeriodicalIF":0.0000,"publicationDate":"1984-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"144","resultStr":"{\"title\":\"Bartlett Adjustments to the Likelihood Ratio Statistic and the Distribution of the Maximum Likelihood Estimator\",\"authors\":\"O. Barndorff-Nielsen, D. Cox\",\"doi\":\"10.1111/J.2517-6161.1984.TB01321.X\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"For rather general parametric models, a simple connection is established between the Bartlett adjustment factor of the log-likelihood ratio statistic and the normalizing constant c of the formula c I I 1?2L for the conditional distribution of a maximum likelihood estimator as applied to the full model and the model of the hypothesis tested. This leads to a relatively simple demonstration that division of the likelihood ratio statistic by a suitable constant or estimated factor improves the chi-squared approximation to its distribution. Various expressions for these quantities are discussed. In particular, for the case of a one-dimensional parameter an approximation to the constants involved is derived, which does not require integration over the sample space.\",\"PeriodicalId\":17425,\"journal\":{\"name\":\"Journal of the royal statistical society series b-methodological\",\"volume\":\"27 4\",\"pages\":\"483-495\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1984-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"144\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of the royal statistical society series b-methodological\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1111/J.2517-6161.1984.TB01321.X\",\"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 the royal statistical society series b-methodological","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1111/J.2517-6161.1984.TB01321.X","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 144
摘要
对于相当一般的参数模型,对数似然比统计量的Bartlett调整因子与公式c I I 1?的规范化常数c之间建立了简单的联系。2L为条件分布的极大似然估计量,应用于完整模型和模型的假设检验。这导致了一个相对简单的演示,即用合适的常数或估计因子来划分似然比统计量可以改善其分布的卡方近似。讨论了这些量的各种表达式。特别地,对于一维参数的情况,导出了所涉及的常数的近似值,它不需要在样本空间上积分。
Bartlett Adjustments to the Likelihood Ratio Statistic and the Distribution of the Maximum Likelihood Estimator
For rather general parametric models, a simple connection is established between the Bartlett adjustment factor of the log-likelihood ratio statistic and the normalizing constant c of the formula c I I 1?2L for the conditional distribution of a maximum likelihood estimator as applied to the full model and the model of the hypothesis tested. This leads to a relatively simple demonstration that division of the likelihood ratio statistic by a suitable constant or estimated factor improves the chi-squared approximation to its distribution. Various expressions for these quantities are discussed. In particular, for the case of a one-dimensional parameter an approximation to the constants involved is derived, which does not require integration over the sample space.