High dimensional structured additive regression models: Bayesian regularization, smoothing and predictive performance

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Review and evaluation of penalised regression methods for risk prediction in low‐dimensional data with few events (2015, background): “In particular, the marginal priors of Bayesian ridge and lasso are quite close after integrating out the hyperparameters, and the two tend to perform very similarly [35].”
GENERALIZED ADDITIVE PARTIAL LINEAR MODELS WITH HIGH-DIMENSIONAL COVARIATES (2013, methodology): “Such a strategy has been used in the literature, for example, Du, Ma, and Liang (2010) and Kneib, Konrath, and Fahrmeir (2011).”
Generalized additive models for location, scale and shape for high dimensional data—a flexible approach based on boosting (2012, methodology): “In accordance with the results of Kneib et al. (2010), the inclusion of all available covariates in high dimensional models pays off with respect to increasing prediction accuracy.”
Twenty years of P-splines (2015, unknown): “Also considering geoadditive structure, Kneib et al. (2011) expanded and unified Bayesian STAR models to further accommodate high-dimensional covariates.”