Takeuchi's Information Criteria as a form of Regularization

Takeuchi's Information Criteria (TIC) is a linearization of maximum likelihood estimator bias which shrinks the model parameters towards the maximum entropy distribution, even when the model is mis-specified. In statistical machine learning, $L_2$ regularization (a.k.a. ridge regression) also introduces a parameterized bias term with the goal of minimizing out-of-sample entropy, but generally requires a numerical solver to find the regularization parameter. This paper presents a novel regularization approach based on TIC; the approach does not assume a data generation process and results in a higher entropy distribution through more efficient sample noise suppression. The resulting objective function can be directly minimized to estimate and select the best model, without the need to select a regularization parameter, as in ridge regression. Numerical results applied to a synthetic high dimensional dataset generated from a logistic regression model demonstrate superior model performance when using the TIC based regularization over a $L_1$ and a $L_2$ penalty term

1-loop matching and NNLL resummation for all partonic 2 to 2 processes in QCD

The Wilson Coefficients for all 4-parton operators which arise in matching QCD to Soft-Collinear Effective Theory (SCET) are computed at 1-loop. Any dijet observable calculated in SCET beyond leading order will require these results. The Wilson coefficients are separated by spin and color, although most applications will involve only the spin-averaged hard functions. The anomalous dimensions for the Wilson coefficients are given to 2-loop order, and the renormalization group equations are solved explicitly. This will allow for analytical resummation of dijet observables to next-to-next-to-leading logarithmic accuracy. For each channel, there is a natural basis in which the evolution is diagonal in color space. The same basis also diagonalizes the color evolution for the soft function. Even though soft functions required for SCET calculations are observable dependent, it is shown that their renormalization group evolution is almost completely determined by a universal structure. With these results, it will be possible to calculate hadronic event shapes or other dijet observables to next-to-leading order with next-to-next-to-leading log resummation.Comment: 28 pages, 5 tables; v2: typo corrected in Eq. (56