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A new way to order independent components

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  • Saima Afzal
  • Muhammad Mutahir Iqbal

Abstract

A relatively newer computational technique adopted by statisticians is known as independent component analysis (ICA) which is used to analyze complex multidimensional data with the objective to separate it into components that are independent to each other. Quite often the main interest for conducting ICA is to identify a small number of significant independent components (ICs) to replace the original complex dimensions with. For this, determining the order of identified ICs is a pre-requisite. The area is not unaddressed but it does deserve a careful revisiting. This is the subject matter of the paper which introduces a new method to order ICs. The proposed method is based upon regression approach. It compares the magnitude of the mixing coefficients and regression coefficients of the regression of the original series on ICs. Their compatibility determines the order.

Suggested Citation

  • Saima Afzal & Muhammad Mutahir Iqbal, 2016. "A new way to order independent components," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(9), pages 1753-1764, July.
    • Handle: RePEc:taf:japsta:v:43:y:2016:i:9:p:1753-1764
    DOI: 10.1080/02664763.2015.1120709
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    References listed on IDEAS

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    1. Chen, Ray-Bing & Chen, Ying & Härdle, Wolfgang K., 2014. "TVICA—Time varying independent component analysis and its application to financial data," Computational Statistics & Data Analysis, Elsevier, vol. 74(C), pages 95-109.
    2. Chen, Ying & Härdle, Wolfgang & Spokoiny, Vladimir, 2010. "GHICA -- Risk analysis with GH distributions and independent components," Journal of Empirical Finance, Elsevier, vol. 17(2), pages 255-269, March.
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