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Computationally efficient banding of large covariance matrices for ordered data and connections to banding the inverse Cholesky factor. (English) Zbl 1292.62082
Summary: We propose a computationally efficient approach to estimate (large) \(p\)-dimensional covariance matrices of ordered (or longitudinal) data based on an independent sample of size \(n\). To do this, we construct the estimator based on a \(k\)-band partial autocorrelation matrix with the number of bands chosen using an exact multiple hypothesis testing procedure. This approach is considerably faster than many existing methods and only requires inversion of \((k + 1)\)-dimensional covariance matrices. The resulting estimator is positive definite as long as \(k < n\) (where \(p\) can be larger than \(n\)). We make connections between this approach and banding the Cholesky factor of the modified Cholesky decomposition of the inverse covariance matrix (Wu and Pourahmadi, 2003) and show that the maximum likelihood estimator of the \(k\)-band partial autocorrelation matrix is the same as the \(k\)-band inverse Cholesky factor. We evaluate our estimator via extensive simulations and illustrate the approach using high-dimensional sonar data.

MSC:
62H12 Estimation in multivariate analysis
62H15 Hypothesis testing in multivariate analysis
62H20 Measures of association (correlation, canonical correlation, etc.)
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