| A BOOTSTRAP RESAMPLING SCHEME FOR USING THE CANONICAL CORRELATION TECHNIQUE IN RANK ESTIMATION |
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| 作者姓名： | XIN M.TU |
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| 作者单位： | Harvard School |
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| 摘 要： | Rank estimation by canonical correlation analysis in multivariate statistics has been proposed as analternative approach for estimating the number of components in a multicomponent mixture.Amethodological turning point of this new approach is that it focuses on the difference in structure ratherthan in magnitude in characterizing the difference between the signal and the noise.This structuraldifference is quantified through the analysis of canonical correlation,which is a well-established datareduction technique in multivariate statistics.Unfortunately,there is a price to be paid for having thisstructural difference:at least two replicate data matrices are needed to carry out the analysis.In this paper we continue to explore the potential and to extend the scope of the canonical correlationtechnique.In particular,we propose a bootstrap resampling method which makes it possible to performthe canonical correlation analysis on a single data matrix.Since a robust estimator is introduced to makeinference about the rank,the procedure may be applied to a wide range of data without any restrictionon the noise distribution.Results from real as well as simulated mixture samples indicate that when usedin conjunction with this resampling method,canonical correlation analysis of a single data matrix isequally efficient as of replicate data matrices.
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A BOOTSTRAP RESAMPLING SCHEME FOR USING THE CANONICAL CORRELATION TECHNIQUE IN RANK ESTIMATION |
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| XIN M.TU.A BOOTSTRAP RESAMPLING SCHEME FOR USING THE CANONICAL CORRELATION TECHNIQUE IN RANK ESTIMATION[J].Journal of Geographical Sciences,1991(4). |
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| Authors: | XIN MTU Harvard School of Public Health |
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| Institution: | XIN M.TU Harvard School of Public Health,Department of Biostatistics,Huntington Ave,Boston,MA,U.S.A. |
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| Abstract: | Rank estimation by canonical correlation analysis in multivariate statistics has been proposed as an alternative approach for estimating the number of components in a multicomponent mixture.A methodological turning point of this new approach is that it focuses on the difference in structure rather than in magnitude in characterizing the difference between the signal and the noise.This structural difference is quantified through the analysis of canonical correlation,which is a well-established data reduction technique in multivariate statistics.Unfortunately,there is a price to be paid for having this structural difference:at least two replicate data matrices are needed to carry out the analysis. In this paper we continue to explore the potential and to extend the scope of the canonical correlation technique.In particular,we propose a bootstrap resampling method which makes it possible to perform the canonical correlation analysis on a single data matrix.Since a robust estimator is introduced to make inference about the rank,the procedure may be applied to a wide range of data without any restriction on the noise distribution.Results from real as well as simulated mixture samples indicate that when used in conjunction with this resampling method,canonical correlation analysis of a single data matrix is equally efficient as of replicate data matrices. |
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| Keywords: | Rank estimation Bootstrap resampling Canonical correlation Excitation-emission matrix Singular value decomposition |
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