Properties of principal component methods for functional and longitudinal data analysis
نویسندگان
چکیده
منابع مشابه
Properties of Principal Component Methods for Functional and Longitudinal Data Analysis by Peter Hall,
The use of principal component methods to analyze functional data is appropriate in a wide range of different settings. In studies of “functional data analysis,” it has often been assumed that a sample of random functions is observed precisely, in the continuum and without noise. While this has been the traditional setting for functional data analysis, in the context of longitudinal data analys...
متن کاملProperties of Principal Component Methods for Functional and Longitudinal Data Analysis
The use of principal component methods to analyze functional data is appropriate in a wide range of different settings. In studies of “functional data analysis,” it has often been assumed that a sample of random functions is observed precisely, in the continuum and without noise. While this has been the traditional setting for functional data analysis, in the context of longitudinal data analys...
متن کاملFunctional Principal Component Analysis for Longitudinal and Survival Data
This paper proposes a nonparametric approach for jointly modelling longitudinal and survival data using functional principal components. The proposed model is data-adaptive in the sense that it does not require pre-specified functional forms for longitudinal trajectories and it automatically detects characteristic patterns. The longitudinal trajectories observed with measurement error are repre...
متن کاملProperties of Principal Component Methods for Functional and Longitudinal Data Analysis1
The use of principal components methods to analyse functional data is appropriate in a wide range of different settings. In studies of “functional data analysis”, it has often been assumed that a sample of random functions is observed precisely, in the continuum and without noise. While this has been the traditional setting for functional data analysis, in the context of longitudinal data analy...
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We describe a principal component analysis (PCA) method for functional magnetic resonance imaging (fMRI) data based on functional data analysis, an advanced nonparametric approach. The data delivered by the fMRI scans are viewed as continuous functions of time sampled at the interscan interval and subject to observational noise, and are used accordingly to estimate an image in which smooth func...
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ژورنال
عنوان ژورنال: The Annals of Statistics
سال: 2006
ISSN: 0090-5364
DOI: 10.1214/009053606000000272