The paper introduces spline wavelets as a modelling tool for system identification and proposes the technique of consistent output prediction using wavelets for estimating system parameters. It suggests that direct weighted summation of projections in approximation space could be used for deriving consistent output prediction in case model structure is built with spline wavelets. This can be vi...

We revisit the additive model learning literature and adapt a penalized spline formulation due to Eilers and Marx [4], to train additive classifiers efficiently. We also propose two new embeddings based two classes of orthogonal basis with orthogonal derivatives, which can also be used to efficiently learn additive classifiers. This paper follows the popular theme in the current literature wher...

In this paper we study penalized regression splines (P-splines), which are low–order basis splines with a penalty to avoid undersmoothing. Such P–splines are typically not spatially adaptive, and hence can have trouble when functions are varying rapidly. Our approach is to model the penalty parameter inherent in the P–spline method as a heteroscedastic regression function. We develop a full Bay...

Corporate probability of default (PD) prediction is vitally important for risk management and asset pricing. In search accurate PD prediction, we propose a flexible yet easy-to-interpret default-prediction single-index hazard model (DSI). By applying it to comprehensive U.S. corporate bankruptcy database constructed, discover an interesting V-shaped relationship, indicating violation the common...

Modeling time series (TS) is a research focus in cryospheric sciences because of the complexity and multiscale nature events interest. Highly nonuniform sampling measurements from different sensors with levels accuracy, as typical for ice sheet elevations, makes problem even more challenging. In this article, we propose spline-based approximation framework (ALPS—Approximation by Localized Penal...

Traditionally, spline or kernel approaches in combination with parametric estimation are used to infer the linear coefficient (fixed effects) a partially mixed-effects model for repeated measurements. Using machine learning algorithms allows us incorporate complex interaction structures, nonsmooth terms, and high-dimensional variables. The variables response adjusted nonparametrically nonlinear...