A calibration method for non-positive definite covariance matrix in multivariate data analysis

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Covariance matrices that fail to be positive definite arise often in covariance estimation. Approaches addressing this problem exist, but are not well supported theoretically. In this paper, we propose a unified statistical and numerical matrix calibration, finding the optimal positive definite surrogate in the sense of Frobenius norm. The proposed algorithm can be directly applied to any estimated covariance matrix. Numerical results show that the calibrated matrix is typically closer to the true covariance, while making only limited changes to the original covariance structure.

Bibliographical metadata

Original languageEnglish
JournalJournal of Multivariate Analysis
Early online date10 Mar 2017
StatePublished - 2017