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تسجيل مستخدم جديدEstimation of Large Precision Matrices Through Block Penalization
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Clifford Lam
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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 off-diagonal 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 one-step estimator, and prove an oracle property which consists of a notion of block sign-consistency and asymptotic normality. In particular, provided the initial estimator of the Cholesky factor is good enough and the true Cholesky has finite number of non-zero off-diagonal bands, oracle property holds for the one-step 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 $P(|y_j| > x) leq Kexp(-Cx^d)$, $d > 0$, 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 one-step 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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