Results 31 to 40 of about 365,137 (249)
Promet (Zagreb), 2022 In traffic monitoring data analysis, the magnitude of traffic density plays an important role in determining the level of traffic congestion. This study proposes a data imputation method for spatio-functional principal component analysis (s-FPCA) and ...
Bing Tang, Yao Hu, Huan Chen
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Object-Oriented Software for Functional Data
Journal of Statistical Software, 2020 This paper introduces the funData R package as an object-oriented implementation of functional data. It implements a unified framework for dense univariate and multivariate functional data on one- and higher dimensional domains as well as for irregular ...
Clara Happ-Kurz
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The impact of lockdown timing on COVID-19 transmission across US counties
EClinicalMedicine, 2021 Background: Many countries have implemented lockdowns to reduce COVID-19 transmission. However, there is no consensus on the optimal timing of these lockdowns to control community spread of the disease.
Xiaolin Huang +5 more
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Interpretable Functional Principal Component Analysis
Biometrics, 2015 SummaryFunctional principal component analysis (FPCA) is a popular approach to explore major sources of variation in a sample of random curves. These major sources of variation are represented by functional principal components (FPCs). The intervals where the values of FPCs are significant are interpreted as where sample curves have major variations ...
Lin, Zhenhua +2 more
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Multi-dimensional functional principal component analysis [PDF]
Statistics and Computing, 2016 Functional principal component analysis is one of the most commonly employed approaches in functional and longitudinal data analysis and we extend it to analyze functional/longitudinal data observed on a general $d$-dimensional domain. The computational issues emerging in the extension are fully addressed with our proposed solutions.
Lu-Hung Chen, Ci-Ren Jiang
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Eigen-Adjusted Functional Principal Component Analysis
Journal of Computational and Graphical Statistics, 2022 Functional Principal Component Analysis (FPCA) has become a widely-used dimension reduction tool for functional data analysis. When additional covariates are available, existing FPCA models integrate them either in the mean function or in both the mean function and the covariance function.
Ci-Ren Jiang +3 more
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Parametric Functional Principal Component Analysis
Biometrics, 2017 Summary Functional principal component analysis (FPCA) is a popular approach in functional data analysis to explore major sources of variation in a sample of random curves. These major sources of variation are represented by functional principal components (FPCs).
Sang, Peijun +2 more
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Landmark mediation survival analysis using longitudinal surrogate
Frontiers in Oncology, 2023 Clinical cancer trials are designed to collect radiographic measurements of each patient’s baseline and residual tumor burden at regular intervals over the course of study.
Jie Zhou +4 more
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Conditional Functional Principal Components Analysis [PDF]
Scandinavian Journal of Statistics, 2006 Abstract. This work proposes an extension of the functional principal components analysis (FPCA) or Karhunen–Loève expansion, which can take into account non‐parametrically the effects of an additional covariate. Such models can also be interpreted as non‐parametric mixed effect models for functional data.
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Functional principal components analysis of workload capacity functions [PDF]
Behavior Research Methods, 2013 Workload capacity, an important concept in many areas of psychology, describes processing efficiency across changes in workload. The capacity coefficient is a function across time that provides a useful measure of this construct. Until now, most analyses of the capacity coefficient have focused on the magnitude of this function, and often only in terms
Burns, Devin Michael +3 more
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