A flexible nonparametric regression model is considered in which the response depends linearly on some covariates, with regression coefficients as additive functions of other covariates. Polynomial spline estimators are proposed for the unknown coefficient functions, with optimal univariate mean square convergence rate under geometric mixing condition. Consistent model selection method is also ...

Twenty-two decision tree, nine statistical, and two neural network algorithms are compared on thirty-two datasets in terms of classi cation accuracy, training time, and (in the case of trees) number of leaves. Classi cation accuracy is measured by mean error rate and mean rank of error rate. Both criteria place a statistical, spline-based, algorithm called Polyclass at the top, although it is n...

Identification of prognostic and predictive biomarkers is important for targeting treatments to patients and for the design and analysis of randomised controlled trials. Cox proportional hazards modelling is a standard method for assessing prognostic value of clinical biomarkers where time to occurrence of an event is the primary outcome of interest. An important issue in the analysis of progno...

Quasi-hierarchical Powell-Sabin splines are C-continuous quadratic splines defined on a locally refined hierarchical triangulation. They admit a compact representation in a normalized B-spline basis. We prove that the quasi-hierarchical basis is in general weakly Lpstable, but for a broad class of hierarchical triangulations it is even strongly Lp-stable.

Deformable models are used to recognize handwritten characters which have a great variety of handwriting styles. The overall character shape is modeled by a B-spline and individual pixels are modeled by Gaussian functions. Model parameters associated with the spline and the Gaussian functions, together with their relative strength, are estimated using Bayesian inference. Under such a Bayesian f...

Traffic flow data are routinely collected for many networks worldwide. These invariably large data sets can be used as part of a traffic management system, for which good traffic flow forecasting models are crucial. The linear multiregression dynamic model (LMDM) has been shown to be promising for forecasting flows, accommodating multivariate flow time series, while being a computationally simp...

This paper addresses the problem of navigating a robotic vehicle over non-planar surfaces for the purpose of performing visual surveys. The key issue lies in generating a spatiallysmooth camera orientation command that will point the camera toward the surface during surface traversal. The proposed solution filters high spatial frequency local surface measurements using a low spatial frequency s...

This article proposes a new method for the joint clustering of functional predictors with some outcome of interest. A multivariate adaptive spline model is used to describe the functions, and the outcome is modeled through a generalized linear model with a random intercept. Through specifying the random intercept to follow a Dirichlet process jointly with the random spline coefficients, we obta...

This paper presents a multi-scale generative model for representing animate shapes and extracting meaningful parts of objects. The model assumes that animate shapes (2D simple closed curves) are formed by a linear superposition of a number of shape bases. These shape bases resemble the multi-scale Gabor bases in image pyramid representation, are well localized in both spatial and frequency doma...

Traffic flow data are routinely collected for many networks worldwide. These invariably large data sets can be used as part of a traffic management system, for which good traffic flow forecasting models are crucial. The linear multiregression dynamic model (LMDM) has been shown to be promising for forecasting flows, accommodating multivariate flow time series, while being a computationally simp...