Results 31 to 40 of about 1,020,465 (280)
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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IEEE Access, 2020 In order to guarantee and improve the product quality, the data-driven fault detection technique has been widely used in industry. For three-way datasets of batch process in industry process (i.e., batch × variable × time), a novel method ...
Fei He, Zhiyan Zhang
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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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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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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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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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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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Functional principal component analysis of spatially correlated data [PDF]
, 2016 This paper focuses on the analysis of spatially correlated functional data. We propose a parametric model for spatial correlation and the between-curve correlation is modeled by correlating functional principal component scores of the functional data ...
Hooker, Giles, Liu, Chong, Ray, Surajit
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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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Principal Component Analysis for Functional Data [PDF]
, 2001 In functional principal component analysis (PCA), we treat the data that consist of functions not of vectors (Ramsay and Silverman, 1997). It is an attractive methodology, because we often meet the cases where we wish to apply PCA to such data.
Tanaka, Yutaka, Yamanishi, Yoshihiro
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