This article considers spline smoothing of variance functions. We focus on selection of smoothing parameters and develop three direct data-driven methods: unbiased risk (UBR), generalized approximate cross validation (GACV) and generalized maximum likelihood (GML). In addition to guaranteed convergence, simulations show that these direct methods perform better than existing indirect UBR, genera...

In this paper we propose a method for piecewise linear reconstruction and subsequent smoothing of a point sampled curve. The reconstruction step is based on the meshless parameterization reconstruction algorithm proposed by Floater. The information computed in the reconstruction step is used for a least squares based discrete smoothing method, with behavior comparable to a smoothing spline. We ...

A general version of multivariate smoothing splines with correlated errors and correlated curves is proposed. A suitable symmetric smoothing parameter matrix is introduced, and practical priors are developed for the unknown covariance matrix of the errors and the smoothing parameter matrix. An efficient algorithm for computing the multivariate smoothing spline is derived, which leads to an effi...

A reference prior and corresponding reference posteriors are derived for a basic Normal variance components model with two components. Di¤erent parameterizations are considered, in particular one in terms of a shrinkage or smoothing parameter. Earlier results for the one-way ANOVA setting are generalized and a broad range of applications of the general results is indicated. Numerical examples o...

We thank Hansen and Kooperberg (HK) for an interesting paper discussing model selection methods in the context of Extended Linear Models. We comment on their univariate density estimation studies, which maximize the log likelihood in a low dimensional linear space. They consider spline bases for this space and consider greedy and Bayesian methods for choosing the knots. We describe a penalized ...

Bayesian Gaussian processes and Support Vector machines are powerful kernel-based methods to attack the pattern recognition problem. Probably due to the very different philosophies of the fields they have been originally proposed in, techniques for these two models have been developed somewhat in isolation from each other. This tutorial paper reviews relationships between Bayesian Gaussian proc...

Microarray data acquired during time-course experiments allow the temporal variations in gene expression to be monitored. An original postprandial fasting experiment was conducted in the mouse and the expression of 200 genes was monitored with a dedicated macroarray at 11 time points between 0 and 72 hours of fasting. The aim of this study was to provide a relevant clustering of gene expression...

In this paper, we propose control theoretic smoothing splines with L optimality for reducing the number of parameters that describes the fitted curve as well as removing outlier data. A control theoretic spline is a smoothing spline that is generated as an output of a given linear dynamical system. Conventional design requires exactly the same number of base functions as given data, and the res...

In this paper we introduce a new class of diffeomorphic smoothers based on general spline smoothing techniques and on the use of some tools that have been recently developed in the context of image warping to compute smooth diffeomorphisms. This diffeomorphic spline is defined as the solution of an ordinary differential equation governed by an appropriate time-dependent vector field. This solut...

'Endpoint error' describes the erratic behavior at the beginning and end of the computed acceleration data which is commonly observed after smoothing and differentiating raw displacement data. To evaluate endpoint error produced by four popular smoothing and differentiating techniques, Lanshammar's (1982, J. Biomechanics 15, 99-105) modification of the Pezzack et al. (1977, J. Biomechanics, 10,...