A wavelet-based method in aggregated functional data analysis

IF 0.8 Q3 STATISTICS & PROBABILITY Monte Carlo Methods and Applications Pub Date : 2023-10-14 DOI:10.1515/mcma-2023-2016
Alex Rodrigo dos Santos Sousa
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引用次数: 1

Abstract

Abstract In this paper, we consider aggregated functional data composed by a linear combination of component curves and the problem of estimating these component curves. We propose the application of a bayesian wavelet shrinkage rule based on a mixture of a point mass function at zero and the logistic distribution as prior to wavelet coefficients to estimate mean curves of components. This procedure has the advantage of estimating component functions with important local characteristics such as discontinuities, spikes and oscillations for example, due the features of wavelet basis expansion of functions. Simulation studies were done to evaluate the performance of the proposed method, and its results are compared with a spline-based method. An application on the so-called Tecator dataset is also provided.
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基于小波的聚合功能数据分析方法
摘要本文考虑由线性组合的分量曲线组成的聚合函数数据,以及这些分量曲线的估计问题。我们提出了一种基于零点质量函数和小波系数前的logistic分布混合的贝叶斯小波收缩规则的应用,以估计分量的平均曲线。由于函数的小波基展开性,该方法具有估计具有重要局部特征(如不连续、尖峰和振荡)的分量函数的优点。仿真研究了该方法的性能,并将其结果与基于样条的方法进行了比较。还提供了一个关于所谓的Tecator数据集的应用程序。
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来源期刊
Monte Carlo Methods and Applications
Monte Carlo Methods and Applications STATISTICS & PROBABILITY-
CiteScore
1.20
自引率
22.20%
发文量
31
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