{"title":"迪里夏特分布的渐近有效闭式估计器","authors":"Jae Ho Chang, Sang Kyu Lee, Hyoung-Moon Kim","doi":"10.1002/sta4.640","DOIUrl":null,"url":null,"abstract":"Maximum likelihood estimator (MLE) of the Dirichlet distribution is usually obtained by using the Newton–Raphson algorithm. However, in some cases, the computational costs can be burdensome, for example, in real-time processes. Therefore, it is beneficial to develop a closed-form estimator that is as efficient as the MLE for large sample. Here, we suggest asymptotically efficient closed-form estimator based on the classical large sample theory.","PeriodicalId":56159,"journal":{"name":"Stat","volume":"82 1","pages":""},"PeriodicalIF":0.7000,"publicationDate":"2023-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An asymptotically efficient closed-form estimator for the Dirichlet distribution\",\"authors\":\"Jae Ho Chang, Sang Kyu Lee, Hyoung-Moon Kim\",\"doi\":\"10.1002/sta4.640\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Maximum likelihood estimator (MLE) of the Dirichlet distribution is usually obtained by using the Newton–Raphson algorithm. However, in some cases, the computational costs can be burdensome, for example, in real-time processes. Therefore, it is beneficial to develop a closed-form estimator that is as efficient as the MLE for large sample. Here, we suggest asymptotically efficient closed-form estimator based on the classical large sample theory.\",\"PeriodicalId\":56159,\"journal\":{\"name\":\"Stat\",\"volume\":\"82 1\",\"pages\":\"\"},\"PeriodicalIF\":0.7000,\"publicationDate\":\"2023-12-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Stat\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://doi.org/10.1002/sta4.640\",\"RegionNum\":4,\"RegionCategory\":\"数学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"STATISTICS & PROBABILITY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Stat","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1002/sta4.640","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
An asymptotically efficient closed-form estimator for the Dirichlet distribution
Maximum likelihood estimator (MLE) of the Dirichlet distribution is usually obtained by using the Newton–Raphson algorithm. However, in some cases, the computational costs can be burdensome, for example, in real-time processes. Therefore, it is beneficial to develop a closed-form estimator that is as efficient as the MLE for large sample. Here, we suggest asymptotically efficient closed-form estimator based on the classical large sample theory.
StatDecision Sciences-Statistics, Probability and Uncertainty
CiteScore
1.10
自引率
0.00%
发文量
85
期刊介绍:
Stat is an innovative electronic journal for the rapid publication of novel and topical research results, publishing compact articles of the highest quality in all areas of statistical endeavour. Its purpose is to provide a means of rapid sharing of important new theoretical, methodological and applied research. Stat is a joint venture between the International Statistical Institute and Wiley-Blackwell.
Stat is characterised by:
• Speed - a high-quality review process that aims to reach a decision within 20 days of submission.
• Concision - a maximum article length of 10 pages of text, not including references.
• Supporting materials - inclusion of electronic supporting materials including graphs, video, software, data and images.
• Scope - addresses all areas of statistics and interdisciplinary areas.
Stat is a scientific journal for the international community of statisticians and researchers and practitioners in allied quantitative disciplines.