Penalized maximum likelihood estimation and effective dimension

This paper extends some prominent statistical results including \emph{Fisher Theorem and Wilks phenomenon} to the penalized maximum likelihood estimation with a quadratic penalization. It appears that sharp expansions for the penalized MLE $\tilde{\thetav}_{G}$ and for the penalized maximum likelihood can be obtained without involving any asymptotic arguments, the results only rely on smoothness and regularity properties of the of the considered log-likelihood function. The error of estimation is specified in terms of the effective dimension $p_G$ of the parameter set which can be much smaller than the true parameter dimension and even allows an infinite dimensional functional parameter. In the i.i.d. case, the Fisher expansion for the penalized MLE can be established under the constraint "$p_G^{2}/n$ is small" while the remainder in the Wilks result is of order $p_G^{3}/n$.