{"title":"线性自回归学习的简短信息理论分析","authors":"Ingvar Ziemann","doi":"arxiv-2409.06437","DOIUrl":null,"url":null,"abstract":"In this note, we give a short information-theoretic proof of the consistency\nof the Gaussian maximum likelihood estimator in linear auto-regressive models.\nOur proof yields nearly optimal non-asymptotic rates for parameter recovery and\nworks without any invocation of stability in the case of finite hypothesis\nclasses.","PeriodicalId":501175,"journal":{"name":"arXiv - EE - Systems and Control","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Short Information-Theoretic Analysis of Linear Auto-Regressive Learning\",\"authors\":\"Ingvar Ziemann\",\"doi\":\"arxiv-2409.06437\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this note, we give a short information-theoretic proof of the consistency\\nof the Gaussian maximum likelihood estimator in linear auto-regressive models.\\nOur proof yields nearly optimal non-asymptotic rates for parameter recovery and\\nworks without any invocation of stability in the case of finite hypothesis\\nclasses.\",\"PeriodicalId\":501175,\"journal\":{\"name\":\"arXiv - EE - Systems and Control\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-09-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - EE - Systems and Control\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2409.06437\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - EE - Systems and Control","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.06437","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Short Information-Theoretic Analysis of Linear Auto-Regressive Learning
In this note, we give a short information-theoretic proof of the consistency
of the Gaussian maximum likelihood estimator in linear auto-regressive models.
Our proof yields nearly optimal non-asymptotic rates for parameter recovery and
works without any invocation of stability in the case of finite hypothesis
classes.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
