Akaike's information criterion and recent developments in information complexity
Journal of Mathematical Psychology
Improving nonparametric regression methods by bagging and boosting
Computational Statistics & Data Analysis - Nonlinear methods and data mining
On the degrees of freedom in shrinkage estimation
Journal of Multivariate Analysis
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The degrees of freedom of semiparametric additive monotone models are derived using results about projections onto sums of order cones. Two important related questions are also studied, namely, the definition of estimators for the parameter of the error term and the formulation of specific Akaike Information Criteria statistics. Several alternatives are proposed to solve both problems and simulation experiments are conducted to compare the behavior of the different candidates. A new selection criterion is proposed that combines the ability to guess the model but also the efficiency to estimate the variance parameter. Finally, the criterion is used to select the model in a regression problem from a well known data set.