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

作者：Claeskens Gerda^*; Silverman Bernard W; Slaets Leen

来源：Journal of the Royal Statistical Society - Series B: Statistical Methodology , 2010, 72: 673-694.

DOI：10.1111/j.1467-9868.2010.00752.x

• Amplitude misalignment
• Bayesian inference
• Functional data analysis
• Markov chain Monte Carlo sampling
• Time warping
• Warping components
• Warping function

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.

• 出版日期2010