Pakize Taylan

TL;DR: In this article, the authors used a penalized residual sum of squares for MARS as a Tikhonov regularization problem, and treated this with continuous optimization technique, in particular, the framework of conic quadratic programming.

TL;DR: Weber et al. as discussed by the authors considered two types of parameters dependency, linear and nonlinear, by constructing a penalized residual sum of squares and investigating the related Tikhonov regularization problem for the first one.

TL;DR: This study model and treat the constrained residual sum of squares by the elegant framework of conic quadratic programming and contributes to regression with generalized additive models by bounding (penalizing) second-order terms (curvature) of the splines, leading to a more robust approximation.

TL;DR: A model-based approach to the important data mining tool Multivariate adaptive regression splines (MARS), which has originally been organized in a more model-free way, is introduced and the performance of the established MARS and the new CMARS in classifying diabetic persons is evaluated, where CMARS turns out to be very competitive and promising.

TL;DR: It is shown that the continuous optimization techniques used in data mining are also very successful in financial theory and application and contribute to further benefits from model-based methods of applied mathematics in the financial sector.