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A memory-based method to select the number of relevant components in Principal Component Analysis

Research output: Contribution to journalArticlepeer-review

Original languageEnglish
JournalJournal of Statistical Mechanics (JSTAT)
Early online date27 Sep 2019
DOIs
Accepted/In press1 Aug 2019
E-pub ahead of print27 Sep 2019
Published2019

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King's Authors

Abstract

We propose a new data-driven method to select the optimal number of relevant components in Principal Component Analysis (PCA). This new method applies to correlation matrices whose time autocorrelation function decays more slowly than an exponential, giving rise to long memory effects. In comparison with other available methods present in the literature, our procedure does not rely on subjective evaluations and is computationally inexpensive. The underlying basic idea is to use a suitable factor model to analyse the residual memory after sequentially removing more and more components, and stopping the process when the maximum amount of memory has been accounted for by the retained components. We validate our methodology on both synthetic and real financial data, and find in all cases a clear and computationally superior answer entirely compatible with available heuristic criteria, such as cumulative variance and cross-validation.

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