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2018 Bayesian inference for spectral projectors of the covariance matrix
Igor Silin, Vladimir Spokoiny
Electron. J. Statist. 12(1): 1948-1987 (2018). DOI: 10.1214/18-EJS1451


Let $X_{1},\ldots ,X_{n}$ be an i.i.d. sample in $\mathbb{R}^{p}$ with zero mean and the covariance matrix ${\boldsymbol{\varSigma }^{*}}$. The classical PCA approach recovers the projector $\boldsymbol{P}^{*}_{\mathcal{J}}$ onto the principal eigenspace of ${\boldsymbol{\varSigma }^{*}}$ by its empirical counterpart $\widehat{\boldsymbol{P}}_{\mathcal{J}}$. Recent paper [24] investigated the asymptotic distribution of the Frobenius distance between the projectors $\|\widehat{\boldsymbol{P}}_{\mathcal{J}}-\boldsymbol{P}^{*}_{\mathcal{J}}\|_{2}$, while [27] offered a bootstrap procedure to measure uncertainty in recovering this subspace $\boldsymbol{P}^{*}_{\mathcal{J}}$ even in a finite sample setup. The present paper considers this problem from a Bayesian perspective and suggests to use the credible sets of the pseudo-posterior distribution on the space of covariance matrices induced by the conjugated Inverse Wishart prior as sharp confidence sets. This yields a numerically efficient procedure. Moreover, we theoretically justify this method and derive finite sample bounds on the corresponding coverage probability. Contrary to [24, 27], the obtained results are valid for non-Gaussian data: the main assumption that we impose is the concentration of the sample covariance $\widehat{\boldsymbol{\varSigma }}$ in a vicinity of ${\boldsymbol{\varSigma }^{*}}$. Numerical simulations illustrate good performance of the proposed procedure even on non-Gaussian data in a rather challenging regime.


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Igor Silin. Vladimir Spokoiny. "Bayesian inference for spectral projectors of the covariance matrix." Electron. J. Statist. 12 (1) 1948 - 1987, 2018.


Received: 1 March 2018; Published: 2018
First available in Project Euclid: 18 June 2018

zbMATH: 06917428
MathSciNet: MR3815302
Digital Object Identifier: 10.1214/18-EJS1451

Primary: 62F15, 62G20, 62H25
Secondary: 62F25


Vol.12 • No. 1 • 2018
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