Automatic Repeated-Loess Decomposition of Data Consisting of Sums of Oscillatory Curves

Authors:
David H. Foster
Affiliations:
Computational Neuroscience Group, Department of Optometry and Neuroscience, University of Manchester Institute of Science and Technology, Manchester M60 1QD, UK. d.h.foster@umist.ac.uk
Venue:
Statistics and Computing
Year:
2002

Citing 3
Cited 0

Independent component analysis, a new concept?

Signal Processing - Special issue on higher order statistics
An information-maximization approach to blind separation and blind deconvolution

Neural Computation
Visualizing Data

Visualizing Data

Quantified Score

Hi-index	0.00

Visualization

Abstract

Repeated loess is a nonparametric procedure that uses progressive smoothing and differencing to decompose data consisting of sums of curves. Smoothing is by locally weighted polynomial regression. Here the procedure was developed so that the decomposition into components was controlled automatically by the number of maxima in each component. The level of smoothing of each component was chosen to maximize the estimated probability of the observed number of maxima. No assumptions were made about the periodicity of components and only very weak assumptions about their shapes. The automatic procedure was applied to simulated data and to experimental data on human visual sensitivity to line orientation.