2016
DOI: 10.1186/s12874-016-0179-2
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Exploring functional data analysis and wavelet principal component analysis on ecstasy (MDMA) wastewater data

Abstract: BackgroundWastewater-based epidemiology (WBE) is a novel approach in drug use epidemiology which aims to monitor the extent of use of various drugs in a community. In this study, we investigate functional principal component analysis (FPCA) as a tool for analysing WBE data and compare it to traditional principal component analysis (PCA) and to wavelet principal component analysis (WPCA) which is more flexible temporally.MethodsWe analysed temporal wastewater data from 42 European cities collected daily over on… Show more

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Cited by 11 publications
(11 citation statements)
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“…1B). A common smoothing parameter was used in both smoothing procedures (21). These 497 and 506 individually fitted curves formed the basis for two separate functional principal component analyses (FPCAs), in which curve shape information was extracted.…”
Section: Extracting Curve Shape Information From Ogtt Glucose Curvesmentioning
confidence: 99%
“…1B). A common smoothing parameter was used in both smoothing procedures (21). These 497 and 506 individually fitted curves formed the basis for two separate functional principal component analyses (FPCAs), in which curve shape information was extracted.…”
Section: Extracting Curve Shape Information From Ogtt Glucose Curvesmentioning
confidence: 99%
“…Independent functional principal component curves describe the important modes of temporal variability in growth across the individual fitted curves. FPCA also reduces the dimensions of the problem by representing functions in terms of a finite set of functions and further functional linear model was used to assess the association between factors and trajectories [ 20 , 22 – 24 , 32 – 36 ]. The conditional kernel density estimators plot was used to identify the subgroup of the growth functions and, the proportion of children contributing to each subgroup were estimated.…”
Section: Methodsmentioning
confidence: 99%
“…After publication of the original article [ 1 ], it came to the authors’ attention that there were errors in Fig. 3 , Fig.…”
Section: Erratummentioning
confidence: 99%