{"title":"Corrigendum to the article “Regular multidimensional stationary time series”","authors":"Tamás Szabados","doi":"10.1111/jtsa.12670","DOIUrl":null,"url":null,"abstract":"<p>In Theorem 2.1 which was the main result of the article it was implicitly assumed that for any regular <math>\n <mrow>\n <mi>d</mi>\n </mrow></math>-dimensional weakly stationary time series <math>\n <mrow>\n <mo>{</mo>\n <msub>\n <mrow>\n <mi>X</mi>\n </mrow>\n <mrow>\n <mi>t</mi>\n </mrow>\n </msub>\n <mo>}</mo>\n </mrow></math> of rank <math>\n <mrow>\n <mi>r</mi>\n </mrow></math>, <math>\n <mrow>\n <mn>1</mn>\n <mo>≤</mo>\n <mi>r</mi>\n <mo>≤</mo>\n <mi>d</mi>\n </mrow></math>, there exists an analytic spectral factor <math>\n <mrow>\n <mi>Φ</mi>\n <mo>(</mo>\n <mi>z</mi>\n <mo>)</mo>\n </mrow></math> of the form <math>\n <mrow>\n <mi>Φ</mi>\n <mo>(</mo>\n <msup>\n <mrow>\n <mi>e</mi>\n </mrow>\n <mrow>\n <mo>−</mo>\n <mi>i</mi>\n <mi>ω</mi>\n </mrow>\n </msup>\n <mo>)</mo>\n <mo>=</mo>\n <msqrt>\n <mrow>\n <mn>2</mn>\n <mi>π</mi>\n </mrow>\n </msqrt>\n <mspace></mspace>\n <mover>\n <mrow>\n <mi>U</mi>\n </mrow>\n <mo>˜</mo>\n </mover>\n <mo>(</mo>\n <mi>ω</mi>\n <mo>)</mo>\n <msubsup>\n <mrow>\n <mi>Λ</mi>\n </mrow>\n <mrow>\n <mi>r</mi>\n </mrow>\n <mrow>\n <mn>1</mn>\n <mo>/</mo>\n <mn>2</mn>\n </mrow>\n </msubsup>\n <mo>(</mo>\n <mi>ω</mi>\n <mo>)</mo>\n </mrow></math>, where <math>\n <mrow>\n <msub>\n <mrow>\n <mi>Λ</mi>\n </mrow>\n <mrow>\n <mi>r</mi>\n </mrow>\n </msub>\n <mo>(</mo>\n <mi>ω</mi>\n <mo>)</mo>\n </mrow></math> is the <math>\n <mrow>\n <mi>r</mi>\n <mo>×</mo>\n <mi>r</mi>\n </mrow></math> diagonal matrix of the positive eigenvalues of the spectral density matrix <math>\n <mrow>\n <mi>f</mi>\n <mo>(</mo>\n <mi>ω</mi>\n <mo>)</mo>\n </mrow></math> and <math>\n <mrow>\n <mover>\n <mrow>\n <mi>U</mi>\n </mrow>\n <mo>˜</mo>\n </mover>\n <mo>(</mo>\n <mi>ω</mi>\n <mo>)</mo>\n </mrow></math> is the <math>\n <mrow>\n <mi>d</mi>\n <mo>×</mo>\n <mi>r</mi>\n </mrow></math> sub-unitary matrix of the corresponding eigenvectors. In fact, to the best of my knowledge, it is not known if this assumption is true or false.</p>","PeriodicalId":49973,"journal":{"name":"Journal of Time Series Analysis","volume":"44 3","pages":"331-332"},"PeriodicalIF":1.2000,"publicationDate":"2022-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jtsa.12670","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Time Series Analysis","FirstCategoryId":"100","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/jtsa.12670","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
In Theorem 2.1 which was the main result of the article it was implicitly assumed that for any regular -dimensional weakly stationary time series of rank , , there exists an analytic spectral factor of the form , where is the diagonal matrix of the positive eigenvalues of the spectral density matrix and is the sub-unitary matrix of the corresponding eigenvectors. In fact, to the best of my knowledge, it is not known if this assumption is true or false.
期刊介绍:
During the last 30 years Time Series Analysis has become one of the most important and widely used branches of Mathematical Statistics. Its fields of application range from neurophysiology to astrophysics and it covers such well-known areas as economic forecasting, study of biological data, control systems, signal processing and communications and vibrations engineering.
The Journal of Time Series Analysis started in 1980, has since become the leading journal in its field, publishing papers on both fundamental theory and applications, as well as review papers dealing with recent advances in major areas of the subject and short communications on theoretical developments. The editorial board consists of many of the world''s leading experts in Time Series Analysis.