Covariate Selection for the Semiparametric Additive Risk Model

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High-dimensional additive hazards models and the Lasso (2011, methodology): “When d ≤ n and if Hn is full rank, we can write β̂ = (Hn) −1 hn, see also [19] or [25].”
Coordinate Descent Methods for the Penalized Semiparametric Additive Hazards Model (2012, methodology): “As argued in Martinussen and Scheike (2009), the loss function (6) for the additive hazards model can be interpreted as a ‘prediction error’ within a quite general setting.”
Independent screening for single‐index hazard rate models with ultrahigh dimensional features (2011, methodology): “As discussed by Martinussen and Scheike (2009), the loss function (18) is meaningful for general hazard rate models: it is the empirical version of the mean squared prediction error for predicting, with a working Lin-Ying model, the part of the intensity which is orthogonal to the at-risk indicator.”
A General Theory of Hypothesis Tests and Confidence Regions for Sparse High Dimensional Models (2014, unknown): “To estimate β , [18, 23] proposed the estimator β̂ in (1.”
Handbook of survival analysis (2014, methodology): “This criterion was also suggested independently by Martinussen and Scheike (2009b) that provided further motivation.”