Search results for "Functional Data Analysis"
showing 10 items of 30 documents
Functional Data Analysis and Mixed Effect Models
2004
Panel studies in econometrics as well as longitudinal studies in biomedical applications provide data from a sample of individual units where each unit is observed repeatedly over time (age, etc.). In this context, mixed effect models are often applied to analyze the behavior of a response variable in dependence of a number of covariates. In some important applications it is necessary to assume that individual effects vary over time (age, etc.).
Measuring Dissimilarity Between Curves by Means of Their Granulometric Size Distributions
2008
The choice of a dissimilarity measure between curves is a key point for clustering functional data. Functions are usually pointwise compared and, in many situations, this approach is not appropriate. Mathematical Morphology provides us with a toolbox to overcome this problem. We propose some dissimilarity measures based on morphological granulometries and their performance is evaluated on some functional datasets.
Comparing FPCA Based on Conditional Quantile Functions and FPCA Based on Conditional Mean Function
2019
In this work functional principal component analysis (FPCA) based on quantile functions is proposed as an alternative to the classical approach, based on the functional mean. Quantile regression characterizes the conditional distribution of a response variable and, in particular, some features like the tails behavior; smoothing splines have also been usefully applied to quantile regression to allow for a more flexible modelling. This framework finds application in contexts involving multiple high frequency time series, for which the functional data analysis (FDA) approach is a natural choice. Quantile regression is then extended to the estimation of functional quantiles and our proposal exp…
Functional principal component analysis for multivariate multidimensional environmental data
2015
Data with spatio-temporal structure can arise in many contexts, therefore a considerable interest in modelling these data has been generated, but the complexity of spatio-temporal models, together with the size of the dataset, results in a challenging task. The modelization is even more complex in presence of multivariate data. Since some modelling problems are more natural to think through in functional terms, even if only a finite number of observations is available, treating the data as functional can be useful (Berrendero et al. in Comput Stat Data Anal 55:2619–2634, 2011). Although in Ramsay and Silverman (Functional data analysis, 2nd edn. Springer, New York, 2005) the case of multiva…
Normalizing temporal patterns to analyze sit-to-stand movements by using registration of functional data
2004
Functional data analysis techniques provide an alternative way of representing movement and movement variability as a function of time. In particular, the registration of functional data provides a local normalization of time functions. This normalization transforms a set of curves, records of repeated trials, yielding a new set of curves that only vary in terms of amplitude. Therefore, main events occur at the "same time" for all transformed curves and interesting features of individual recordings remain after averaging processes. This paper presents an application of the registration process to the analysis of the vertical forces exerted on the ground by both feet during the sit-to-stand …
Functional Data Analysis for Optimizing Strategies of Cash-Flow Management
2017
The cash management deals with problem of automating and managing cash-flow processes. Optimization of the management processes greatly reduces overall cash handling costs. The present analysis is an empirical study of cash flows, from and to bank branches, deriving an underlying theoretical framework, which can in a reasonable way be connected with the optimal strategy. Functional data analysis is considered an appropriate framework to analyze the dynamics of the time series behavior of cash flows: since the observations are not equally spaced in time and their number is different for each series, they are converted into a collection of random curves in a space spanned by finite dimensiona…
Functional Data Analysis for Gait Analysis after Stroke
2013
Variability is one of the key determinants of gait after stroke. Functional Data Analysis (FDA) is a suitable tool to deal with variability associated with movement analysis patterns. In this contribution (FDA) has been applied for the analysis 53 post-stroke patients. Functional Principal Components Analysis (FPCA) has been applied. Dependence of velocity on the functional state of the patient has been found as well as other mechanisms that are hidden in conventional parametric analysis of the curves.
Empirical Orthogonal Function and Functional Data Analysis Procedures to Impute Long Gaps in Environmental Data
2016
Air pollution data sets are usually spatio-temporal multivariate data related to time series of different pollutants recorded by a monitoring network. To improve the estimate of functional data when missing values, and mainly long gaps, are present in the original data set, some procedures are here proposed considering jointly Functional Data Analysis and Empirical Orthogonal Function approaches. In order to compare and validate the proposed procedures, a simulation plan is carried out and some performance indicators are computed. The obtained results show that one of the proposed procedures works better than the others, providing a better reconstruction especially in presence of long gaps.
GRANADA consensus on analytical approaches to assess associations with accelerometer-determined physical behaviours (physical activity, sedentary beh…
2021
This study was conducted under the umbrella of the ActiveBrains and the SmarterMove projects supported by the MINECO/FEDER (DEP2013-47540, DEP2016-79512-R, RYC-2011-09011) and the CoCA project supported by the European Union's 2020 research and innovation programme (667302). JHM is supported by a grant from the Spanish Ministry of Education, Culture and Sport (FPU15/02645). AR is supported by the NIHR Leicester Biomedical Research Centre, and the Collaboration for leadership in Applied Health Research and Care (CLAHRC) East Midlands. SS is supported by the French National Research Agency (ANR-19-CE36-0004-01). RW is supported by a Medical Research Council Industrial Strategy Studentship (MR…