Maximum penalized likelihood estimation

By V. LaRiccia, P. Eggermont. 2001

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Maximum likelihood estimation of a multi‐dimensional log‐concave density (2008, background): “, 1996), splines (Eubank, 1988; Wahba, 1990), penalized likelihood (Eggermont and LaRiccia, 2001) and vector support methods (Vapnik and Mukherjee, 2000).”
MAXIMUM SMOOTHED LIKELIHOOD ESTIMATION AND SMOOTHED MAXIMUM LIKELIHOOD ESTIMATION IN THE CURRENT STATUS MODEL (2010, methodology): “Following the general approach of Eggermont and LaRiccia (2001) we replace the empirical distribution IPn in the definition of the log likelihood (2.”
ADAPTIVE SEMI-VARYING COEFFICIENT MODEL SELECTION (2012, background): “, n, are quasi-uniform (Eggermont and LaRiccia (2009)), one can verify that as long as λ0nh −1/2 → 0 but λ0n 3/2 → ∞, the two conditions listed in (2.”
Model selection via testing: an alternative to (penalized) maximum likelihood estimators (2006, background): “leads to similar technical difficulties, as can be seen from [5,21,22,59] or [31].”
On the asymptotics of penalized spline smoothing (2011, methodology): “It is known that the optimal convergence rate of φ̂ at any given inner point is of order n under the optimal choice of λ which is of order n (Eggermont and LaRicca [6]).”