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

• 成果类型: Article
• 署名作者: Claeskens, Gerda; Silverman, Bernard W.; Slaets, Leen
• 署名单位: KU Leuven; KU Leuven; University of Oxford
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/j.1467-9868.2010.00752.x
• 发表日期: 2010
• 页码: 673-694
• 关键词: maximum-likelihood-estimation; sample; REGISTRATION; alignment; CURVES

摘要

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.