A multiresolution approach to time warping achieved by a Bayesian prior-posterior transfer fitting strategy
A multiresolution approach to time warping achieved by a Bayesian prior-posterior transfer fitting strategy
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DOI：
10.1111/j.1467-9868.2010.00752.x
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发表时间：
2010-01-01
影响因子：
5.8
通讯作者：
Slaets, Leen
中科院分区：
文献类型：
--
作者：
Claeskens, Gerda;Silverman, Bernard W.;Slaets, Leen
Warping是一种减少和分析函数观测中相位变化的方法，通过将平滑双射应用于函数自变量。我们提出了一种自然的表示翘曲函数的一种新类型的基本功能命名为“翘曲组件功能”，或“warplets”，这是组合成的翘曲函数的组成。反扭曲函数是平凡的，明确获得。介绍了一种序贯贝叶斯估计策略，该策略拟合一系列模型，并将前一次拟合的后验转换为下一次拟合的先验。模型选择是基于一个翘曲模拟小波阈值，结合贝叶斯推理。
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.