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PLS regression on a stochastic process

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Title: PLS regression on a stochastic process
Authors: Preda, C.1 cpreda@univ-lille2.fr, Saporta, G.2 saporta@cnam.fr
Source: Computational Statistics & Data Analysis. Jan2005, Vol. 48 Issue 1, p149-158. 10p.
Subjects: Least squares, Mathematics, Estimation theory, Curve fitting
Abstract: Partial least squares (PLS) regression on an L2-continuous stochastic process is an extension of the finite set case of predictor variables. The PLS components existence as eigenvectors of some operator and convergence properties of the PLS approximation are proved. The results of an application to stock-exchange data will be compared with those obtained by other methods. [Copyright &y& Elsevier]
Copyright of Computational Statistics & Data Analysis is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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Header DbId: egs
DbLabel: Engineering Source
An: 15551878
AccessLevel: 6
PubType: Periodical
PubTypeId: serialPeriodical
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  Data: PLS regression on a stochastic process
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  Data: <searchLink fieldCode="AR" term="%22Preda%2C+C%2E%22">Preda, C.</searchLink><relatesTo>1</relatesTo><i> cpreda@univ-lille2.fr</i><br /><searchLink fieldCode="AR" term="%22Saporta%2C+G%2E%22">Saporta, G.</searchLink><relatesTo>2</relatesTo><i> saporta@cnam.fr</i>
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  Data: <searchLink fieldCode="JN" term="%22Computational+Statistics+%26+Data+Analysis%22">Computational Statistics & Data Analysis</searchLink>. Jan2005, Vol. 48 Issue 1, p149-158. 10p.
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  Data: Partial least squares (PLS) regression on an <f>L2</f>-continuous stochastic process is an extension of the finite set case of predictor variables. The PLS components existence as eigenvectors of some operator and convergence properties of the PLS approximation are proved. The results of an application to stock-exchange data will be compared with those obtained by other methods. [Copyright &y& Elsevier]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Computational Statistics & Data Analysis is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1016/j.csda.2003.10.003
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      – Code: eng
        Text: English
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        PageCount: 10
        StartPage: 149
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      – SubjectFull: Least squares
        Type: general
      – SubjectFull: Mathematics
        Type: general
      – SubjectFull: Estimation theory
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      – SubjectFull: Curve fitting
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      – TitleFull: PLS regression on a stochastic process
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            NameFull: Preda, C.
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              Text: Jan2005
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              Y: 2005
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