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Using Kurtosis To Assess Significance Of Components From Independent Component Analysis

In PCA eigenvalues determine the order of components. In ICA I am using kurtosis to obtain the ordering. What are some accepted methods to assess the number, (given I have the order) of components that are significant apart from prior knowledge about the signal?

pca
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updated by Craig Tracy • Craig Tracy • written by Eminencenoir • Eminencenoir

2 answers

Check out the Tracy-Widom test of the eigenvalues. We are using it for finding out how many components are "important" in genetic structure analysis.

Christian

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updated by Egon Willighagen Egon Willighagen • written by Genotepes Genotepes

Would You send me a link to Your paper? work so that I have some idea on how to apply it, as my problem is atypical

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written by Eminencenoir • Eminencenoir

Johnstone I (2001) On the distribution of the largest eigenvalue in principal components analysis. Ann Stat 29: 295–327

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written by Genotepes Genotepes

Population Structure and Eigenanalysis, PAterson is also applying the preceding and explains.

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written by Genotepes Genotepes

what if my n>m, what do I do? what distribution do I use?

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written by Eminencenoir • Eminencenoir

See also, I. Johnstone, MULTIVARIATE ANALYSIS AND JACOBI ENSEMBLES: LARGEST EIGENVALUE, TRACY–WIDOM LIMITS AND RATES OF CONVERGENCE, The Annals of Statistics 2008, Vol. 36, No. 6, 2638–2716 DOI: 10.1214/08-AOS605

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written by Craig Tracy • Craig Tracy

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