Special feature: functional data analysis and its applications
Special feature: functional data analysis and its applications
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专题：函数数据分析及其应用
DOI：
10.1007/s41237-019-00081-9
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
H.
中科院分区：
文献类型：
--
作者：
Gonzalez-Rodriguez;G.;and Matsui;H.
函数型数据分析在不同的应用领域，包括生物科学、医学科学和气象学，都受到了相当大的关注。函数数据分析背后的思想之一是将时间过程数据表示为平滑函数，然后从函数数据集合中提取信息。函数是在有限网格上观察的，一个重要的优点是，即使网格点的数量及其具体值因个体而异，也可以分析数据。这个特殊的问题集中在功能数据分析及其应用在各个领域的时间过程数据可以提供。本期刊的目的是通过应用说明这些方法分析功能数据的有效性。Araki和川口（2019）的邀请论文“稀疏PCA的功能逻辑判别及其在结构MRI中的应用”提出了一种监督分类方法，通过组合稀疏主成分来处理高维图像预测因子。这三个贡献的文件如下：Misumi等人。（2019）论文《Multivariate functional clustering and its application to typhoon data》提出了一种用于聚类多个纵向数据的多元非线性混合效应模型，并将该方法应用于台风数据的分析。Takagishi和Yadohisa（2019）的论文“使用t分布进行稳健曲线配准”提出了一种新的功能数据集配准方法，然后将其应用于心电图数据分析。Bouanani等人（2019）论文“渐近正态性的
Functional data analysis has received considerable attention in different fields of application, including bioscience, medical science, and meteorology. One of the ideas behind functional data analysis is to express time-course data as smooth functions and then draw information from the collection of functional data. Functions are observed on a finite grid, and one important advantage is that data can be analyzed even when the number of grid points and its concrete values vary from individual to individual. This special issue focuses on functional data analysis and its applications in various fields where time-course data can be available. The aim is to illustrate the effectiveness of the methodologies to analyze functional data through their applications.This issue consists of one invited and three contributed papers. The invited paper by Araki and Kawaguchi (2019)“Functional logistic discrimination with sparse PCA and its application to the structural MRI” proposes a supervised classification method to handle high-dimensional image predictors by combining sparse principal components. The three contributed papers are as follows: Misumi et al.(2019) paper “Multivariate functional clustering and its application to typhoon data” proposes a multivariate nonlinear mixed effects model for clustering multiple longitudinal data and applies the method to the analysis of typhoon data. Takagishi and Yadohisa (2019) paper “Robust curve registration using the t distribution” proposes a new registration method for a functional data set and then applies it to the electrocardiogram data analysis. Bouanani et al.(2019) paper “Asymptotic normality of
高维统计中一些条件非参数函数参数的渐近正态性
影响因子：
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作者：
Oussama Bouanani;Ali Laksaci;Mustapha Rachdi;Saâdia Rahmani
通讯作者：
Saâdia Rahmani
正则基展开函数主成分分析及其应用
DOI：
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发表时间：
2006
期刊：
Japanese Journal of Applied Statistics vol.35
影响因子：
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作者：
KAYANO;Mitsuhiro;et. al.
通讯作者：
et. al.