A multiresolution approach to time warping achieved by a Bayesian prior–posterior transfer fitting strategy

Gerda Claeskens, Bernard W. Silverman and Leen Slaets

Journal of the Royal Statistical Society Series B, 2010, vol. 72, issue 5, 673-694

Abstract: Summary. Warping is an approach to the reduction and analysis of phase variability in functional observations, by applying a smooth bijection to the function argument. We propose a natural representation of warping functions in terms of a new type of elementary functions named ‘warping component functions’, or ‘warplets’, which are combined into the warping function by composition. The inverse warping function is trivial and explicit to obtain. A sequential Bayesian estimation strategy is introduced which fits a series of models and transfers the posterior of the previous fit into the prior of the next fit. Model selection is based on a warping analogue to wavelet thresholding, combined with Bayesian inference.

Date: 2010
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