Lam, Clifford
(2008)
Estimation of large precision matrices through block penalization.
Cornell University , Ithaca, USA.
Abstract
This paper focuses on exploring the sparsity of the inverse covariance matrix $\bSigma^{1}$, or the precision matrix. We form blocks of parameters based on each offdiagonal band of the Cholesky factor from its modified Cholesky decomposition, and penalize each block of parameters using the $L_2$norm instead of individual elements. We develop a onestep estimator, and prove an oracle property which consists of a notion of block signconsistency and asymptotic normality. In particular, provided the initial estimator of the Cholesky factor is good enough and the true Cholesky has finite number of nonzero offdiagonal bands, oracle property holds for the onestep estimator even if $p_n \gg n$, and can even be as large as $\log p_n = o(n)$, where the data $\y$ has mean zero and tail probability $P(y_j > x) \leq K\exp(Cx^d)$, $d > 0$, and $p_n$ is the number of variables. We also prove an operator norm convergence result, showing the cost of dimensionality is just $\log p_n$. The advantage of this method over banding by Bickel and Levina (2008) or nested LASSO by Levina \emph{et al.} (2007) is that it allows for elimination of weaker signals that precede stronger ones in the Cholesky factor. A method for obtaining an initial estimator for the Cholesky factor is discussed, and a gradient projection algorithm is developed for calculating the onestep estimate. Simulation results are in favor of the newly proposed method and a set of real data is analyzed using the new procedure and the banding method.
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