Statistics for High-Dimensional Data: Methods, Theory and Applications

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The hardness of conditional independence testing and the generalised covariance measure (2018, methodology): “A notable exception is when f and g are sparse linear functions; in this setting, alternative arguments can be used to show the GCM with Lasso regressions has optimal power when Z has a sparse inverse covariance [41, 46].”
Geometric median and robust estimation in Banach spaces (2013, neutral): “1 in [8] that 1 n ∥∥∥X(λ̂ε − λ0)∥∥∥2 ≤ 4ε(2) N(λ0) κ(2)(N(λ0), 3) .”
The adaptive and the thresholded Lasso for potentially misspecified models (and a lower bound for the Lasso) (2011, neutral): “suffices for the initial Lasso to have no false positives on Tǫ∩TX (see [5], Chapter 7).”
The Spike-and-Slab LASSO (2018, methodology): “Here, we adopt the concept of restricted eigenvalues (Bühlmann and van der Geer 2011).”
Confidence intervals and hypothesis testing for high-dimensional regression (2013, background): “Of necessity, we do not provide a complete set of references, and instead refer the reader to [BvdG11] for an in-depth introduction to this area.”