Search results for "functional data"
showing 10 items of 46 documents
Robust estimation of mean electricity consumption curves by sampling for small areas in presence of missing values
2017
In this thesis, we address the problem of robust estimation of mean or total electricity consumption curves by sampling in a finite population for the entire population and for small areas. We are also interested in estimating mean curves by sampling in presence of partially missing trajectories.Indeed, many studies carried out in the French electricity company EDF, for marketing or power grid management purposes, are based on the analysis of mean or total electricity consumption curves at a fine time scale, for different groups of clients sharing some common characteristics.Because of privacy issues and financial costs, it is not possible to measure the electricity consumption curve of eac…
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…
Estimate the mean electricity consumption curve by survey and take auxiliary information into account
2012
In this thesis, we are interested in estimating the mean electricity consumption curve. Since the study variable is functional and storage capacities are limited or transmission cost are high survey sampling techniques are interesting alternatives to signal compression techniques. We extend, in this functional framework, estimation methods that take into account available auxiliary information and that can improve the accuracy of the Horvitz-Thompson estimator of the mean trajectory. The first approach uses the auxiliary information at the estimation stage, the mean curve is estimated using model-assisted estimators with functional linear regression models. The second method involves the au…
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…
FDA dimension reduction techniques and components separation in Fourier-transform infrared spectroscopy
2020
FTIR spectroscopy is a measurement technique used to obtain an infrared spectrum of absorption of a solid (or a liquid or a gas), for the characterization of specific chemical components of materials. When repeated measures are taken on samples of materials, the result is a collection of spectra representing a set of samples from continous functions (signals) defined in the domain of the frequencies. An unifying approach to the study of a collection of FTIR spectra is proposed to deal with the presence of random shifts in the peaks, the identification of representative spectra and finally the characterization of the observed differences: in the functional data framework, the performance of …
Fast Estimation of the Median Covariation Matrix with Application to Online Robust Principal Components Analysis
2017
International audience; The geometric median covariation matrix is a robust multivariate indicator of dispersion which can be extended without any difficulty to functional data. We define estimators, based on recursive algorithms, that can be simply updated at each new observation and are able to deal rapidly with large samples of high dimensional data without being obliged to store all the data in memory. Asymptotic convergence properties of the recursive algorithms are studied under weak conditions. The computation of the principal components can also be performed online and this approach can be useful for online outlier detection. A simulation study clearly shows that this robust indicat…