A new cubic convolution spline interpolation (CCSI )for both one-dimensional (1-D) and two-dimensional (2-D) signals is developed in order to subsample signal and image compression data. The CCSI yields a very accurate algorithm for smoothing. It is also shown that this new and fast smoothing filter for CCSI can be used with the JPEG standard to design an improved JPEG encoder-decoder for a hig...

Kalman smoothers obtain state estimates in a system with stochastic dynamics and measurement noise. We consider the smoothing problem in a distributed setting, present a cooperative smoothing algorithm for Gauss-Markov linear models, and provide a convergence analysis for the algorithm. An extension of the algorithm that maximizes the likelihood with respect to a sequence of state vectors subje...

We discuss the construction of C 2 cubic spline quasi-interpolation schemes defined on a refined partition. These are reduced in terms degrees freedom compared to those existing literature. Namely, we provide rule for reducing them by imposing super-smoothing conditions while preserving full smoothness and precision. In addition, subdivision rules means blossoming. The derived designed express ...

We consider the problem of modeling the mean function in regression. Often there is enough knowledge to model some components of the mean function parametrically. But for other vague and/or nuisance components, it is often desirable to leave them unspecified and to be modeled nonparametrically. In this article, we propose a general class of smoothing spline semi-parametric nonlinear regression ...

This document provides a brief introduction to the gss facilities for nonparametric statistical modeling in a variety of problem settings including regression, density estimation, and hazard estimation. Functional ANOVA decompositions are built into models on product domains, and modeling and inferential tools are provided for tasks such as interval estimates, the “testing” of negligible model ...

We discuss a class of methods for the problem of `soft' classi cation in supervised learning. In `hard' classi cation, it is assumed that any two examples with the same attribute vector will always be in the same class, (or have the same outcome), whereas in `soft' classi cation, two examples with the same attribute vector do not necessarily have the same outcome, but the probability of a parti...

Smoothing Spline ANOVA is a statistical modeling algorithm based on a function decomposition similar to the classical analysis of variance (ANOVA) decomposition and the associated notions of main effect and interaction. It represents a suitable screening technique for detecting important variables (Variable Screening) in a given dataset. We present the mathematical background together with poss...

This work describes an automated negotiation agent called OMAC which was awarded the joint third place in the 2012 Automated Negotiating Agent Competition (ANAC 2012). OMAC, standing for “Opponent Modeling and Adaptive Concession”, combines efficient opponent modeling and adaptive concession making. Opponent modeling is achieved through standard wavelet decomposition and cubic smoothing spline;...

and Applied Analysis 3

In this paper we propose an approach for detection of edges of range images using a local statistics for detection of crease edges. The jump edges are first located using the ratio of the slopes as a measure. For the detection of crease edges, curvature is estimated a t all the data points using the first and second order derivatives of the smoothing spline. The local maximas of curvature are c...