This paper examines determinants of creditor recoveries from defaulted debt instruments. First, we argue that to properly measure a debt instrument’s relative position in a firm’s debt structure, debt pari passu to the instrument must be accounted for. We thus propose a new measure of seniority and find that it is the most important determinant of recovery rates. Second, firm-level variables, e...

Page-based Linear Genetic Programming (GP) is proposed and implemented with two-layer Subset Selection to address a two-class intrusion detection classification problem as defined by the KDD-99 benchmark dataset. By careful adjustment of the relationship between subset layers, over fitting by individuals to specific subsets is avoided. Moreover, efficient training on a dataset of 500,000 patter...

The detailed modelling of stellar oscillations is a powerful approach to characterising stars. However, poor treatment systematics in theoretical models leads misinterpretations Here we propose more principled statistical for the be applied fitting individual mode frequencies with typical model grid. We introduce correlated noise based on Gaussian Process (GP) kernel describe given that frequen...

Extracted single-rooted maxillary teeth were endodontically treated and filled with gutta-percha/AH-26 (GP), Resilon points/RealSeal (RS), AdheSE DC/Multicore Flow (ADH, self-etch control), or Excite DSC/Multicore Flow (EXC, total-etch control). Specimens were analyzed with electron microscopy using three methods: (a) field emission scanning electron microscopy (FESEM) of the interface; (b) tra...

Gaussian process (GP) models are commonly used statistical metamodels for emulating expensive computer simulators. Fitting a GP model can be numerically unstable if any pair of design points in the input space are close together. Ranjan, Haynes, and Karsten (2011) proposed a computationally stable approach for fitting GP models to deterministic computer simulators. They used a genetic algorithm...

Voice conversion (VC) is a technique aiming to mapping the individuality of a source speaker to that of a target speaker, wherein Gaussian mixture model (GMM) based methods are evidently prevalent. Despite their wide use, two major problems remains to be resolved, i.e., over-smoothing and over-fitting. The latter one arises naturally when the structure of model is too complicated given limited ...

Soft computing techniques have been widely used during the last two decades for nonlinear system modeling, specifically as predictive tools. In this study, the performances of two well-known soft computing predictive techniques, artificial neural network (ANN) and genetic programming (GP), are evaluated based on several criteria, including over-fitting potential. A case study in punching shear ...

We introduce a class of nonstationary covariance functions for Gaussian process (GP) regression. Nonstationary covariance functions allow the model to adapt to functions whose smoothness varies with the inputs. The class includes a nonstationary version of the Matérn stationary covariance, in which the differentiability of the regression function is controlled by a parameter, freeing one from f...

P-glycoprotein (P-gp) is an ATP (adenosine triphosphate)-binding cassette transporter that causes multidrug resistance of various chemotherapeutic substances by active efflux from mammalian cells. P-gp plays a pivotal role in limiting drug absorption and distribution in different organs, including the intestines and brain. Thus, the prediction of P-gp-drug interactions is of vital importance in...