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Non-parametric estimation of data dimensionality prior to data compression : the case of the human development index

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Canning, David, French, Declan and Moore, Michael J. (2013) Non-parametric estimation of data dimensionality prior to data compression : the case of the human development index. Journal of Applied Statistics, Volume 40 (Number 9). pp. 1853-1863. doi:10.1080/02664763.2013.798629 ISSN 0266-4763.

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Official URL: http://dx.doi.org/10.1080/02664763.2013.798629

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Abstract

In many applications in applied statistics researchers reduce the complexity of a data set by combining a group of variables into a single measure using factor analysis or an index number. We argue that such compression loses information if the data actually has high dimensionality. We advocate the use of a non-parametric estimator, commonly used in physics (the Takens estimator), to estimate the correlation dimension of the data prior to compression. The advantage of this approach over traditional linear data compression approaches is that the data does not have to be linearized. Applying our ideas to the United Nations Human Development Index we find that the four variables that are used in its construction have dimension three and the index loses information.

Item Type: Journal Article
Subjects: H Social Sciences > HA Statistics
Divisions: Faculty of Social Sciences > Warwick Business School
Library of Congress Subject Headings (LCSH): Statistics -- United Nations, Economic development -- United Nations, Well-being -- United Nations, Well-being -- Economic aspects
Journal or Publication Title: Journal of Applied Statistics
Publisher: Routledge
ISSN: 0266-4763
Official Date: 16 May 2013
Dates:
DateEvent
16 May 2013Published
19 April 2013Accepted
17 February 2012Submitted
Volume: Volume 40
Number: Number 9
Number of Pages: 10
Page Range: pp. 1853-1863
DOI: 10.1080/02664763.2013.798629
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access

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