{"title":"范德蒙德低秩近似的偏差修正","authors":"Antonio Fazzi , Alexander Kukush , Ivan Markovsky","doi":"10.1016/j.ecosta.2021.09.001","DOIUrl":null,"url":null,"abstract":"<div><p>The low-rank approximation problem, that is the problem of approximating a given matrix with a matrix of lower rank, appears in many scientific fields. In some applications the given matrix is structured and the approximation is required to have the same structure. Examples of linear structures are Hankel, Toeplitz, and Sylvester. Currently, there are only a few results for nonlinearly structured low-rank approximation problems. The problem of Vandermonde structured low-rank approximation is considered. The high condition number of the Vandermonde matrix, in combination with the noise in the data, makes the problem challenging. A numerical method based on a bias correction procedure is proposed and its properties are demonstrated by simulation. The performance of the method is illustrated on numerical results.</p></div>","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"31 ","pages":"Pages 38-48"},"PeriodicalIF":2.0000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Bias correction for Vandermonde low-rank approximation\",\"authors\":\"Antonio Fazzi , Alexander Kukush , Ivan Markovsky\",\"doi\":\"10.1016/j.ecosta.2021.09.001\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The low-rank approximation problem, that is the problem of approximating a given matrix with a matrix of lower rank, appears in many scientific fields. In some applications the given matrix is structured and the approximation is required to have the same structure. Examples of linear structures are Hankel, Toeplitz, and Sylvester. Currently, there are only a few results for nonlinearly structured low-rank approximation problems. The problem of Vandermonde structured low-rank approximation is considered. The high condition number of the Vandermonde matrix, in combination with the noise in the data, makes the problem challenging. A numerical method based on a bias correction procedure is proposed and its properties are demonstrated by simulation. The performance of the method is illustrated on numerical results.</p></div>\",\"PeriodicalId\":54125,\"journal\":{\"name\":\"Econometrics and Statistics\",\"volume\":\"31 \",\"pages\":\"Pages 38-48\"},\"PeriodicalIF\":2.0000,\"publicationDate\":\"2024-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Econometrics and Statistics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2452306221001088\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ECONOMICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Econometrics and Statistics","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2452306221001088","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ECONOMICS","Score":null,"Total":0}
Bias correction for Vandermonde low-rank approximation
The low-rank approximation problem, that is the problem of approximating a given matrix with a matrix of lower rank, appears in many scientific fields. In some applications the given matrix is structured and the approximation is required to have the same structure. Examples of linear structures are Hankel, Toeplitz, and Sylvester. Currently, there are only a few results for nonlinearly structured low-rank approximation problems. The problem of Vandermonde structured low-rank approximation is considered. The high condition number of the Vandermonde matrix, in combination with the noise in the data, makes the problem challenging. A numerical method based on a bias correction procedure is proposed and its properties are demonstrated by simulation. The performance of the method is illustrated on numerical results.
期刊介绍:
Econometrics and Statistics is the official journal of the networks Computational and Financial Econometrics and Computational and Methodological Statistics. It publishes research papers in all aspects of econometrics and statistics and comprises of the two sections Part A: Econometrics and Part B: Statistics.