On the inconsistency of ℓ_1-penalised sparse precision matrix estimation
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Various ℓ_1-penalised estimation methods such as graphical lasso and CLIME are widely used for sparse precision matrix estimation. Many of these methods have been shown to be consistent under various quantitative assumptions about the underlying true covariance matrix. Intuitively, these conditions are related to situations where the penalty term will dominate the optimisation. In this paper, we explore the consistency of ℓ_1-based methods for a class of sparse latent variable -like models, which are strongly motivated by several types of applications. We show that all ℓ_1-based methods fail dramatically for models with nearly linear dependencies between the variables. We also study the consistency on models derived from real gene expression data and note that the assumptions needed for consistency never hold even for modest sized gene networks and ℓ_1-based methods also become unreliable in practice for larger networks.
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