Correction: Boosted-oriented probabilistic smoothing-spline clustering of series

Clustering Time-Series Gene Expression Data Using Smoothing Spline Derivatives

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...

Optimal Spline Smoothing of FMRI Time Series

Smoothing splines with generalized cross-validation parameter selection (GCV-spline) provide a method to find an optimal smoother for an fMRI time series. The purpose of this study was to compare the variance of parameter estimates and the bias of the variance estimator for a linear regression model smoothed with GCV-spline and the low-pass filter in SPM99 (SPM-HRF). The mean bias with the SPM-...

Smoothing by Spline Functions

Smoothing an arc spline

Arc splines are G continuous curves made of circular arcs and straight-line segments. They have the advantages that the curvature of an arc spline is known and controlled at all but a finite number of points, and that the offset curve of an arc spline is another arc spline. Arc splines are used by computer-controlled machines as a natural curve along which to cut and are used by highway route p...

Spline Smoothing on Surfaces

We present a method for estimating functions on topologically and/or geometrically complex surfaces from possibly noisy observations. Our approach is an extension of spline smoothing, using a Þnite element method. The paper has a substantial tutorial component: we start by reviewing smoothness measures for functions deÞned on surfaces, simplicial surfaces and differentiable structures on such s...