Search results for "Principal component analysis"
showing 10 items of 486 documents
Functional Principal Components Analysis with Survey Data
2008
This work aims at performing Functional Principal Components Analysis (FPCA) with Horvitz-Thompson estimators when the observations are curves collected with survey sampling techniques. FPCA relies on estimations of the eigenelements of the covariance operator which can be seen as nonlinear functionals. Adapting to our functional context the linearization technique based on the influence function developed by Deville (1999), we prove that these estimators are asymptotically design unbiased and convergent. Under mild assumptions, asymptotic variances are derived for the FPCA’ estimators and convergent estimators of them are proposed. Our approach is illustrated with a simulation study and we…
A functional approach to monitor and recognize patterns of daily traffic profiles
2014
Functional Data Analysis (FDA) is a collection of statistical techniques for the analysis of information on curves or functions. This paper presents a new methodology for analyzing the daily traffic flow profiles based on the employment of FDA. A daily traffic profile corresponds to a single datum rather than a large set of traffic counts. This insight provides ideal information for strategic decision-making regarding road expansion, control, and other long-term decisions. Using Functional Principal Component Analysis the data are projected into a low dimensional space: the space of the first functional principal components. Each curve is represented by their vector of scores on this basis.…
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 air quality indices aggregated by pollutant
2011
In this paper a new aggregate Air Quality Index (AQI) useful for describing the global air pollution situation for a given area is proposed. The index, unlike most of currently used AQIs, takes into account the combined effects of all the considered pollutants to human health. Its good performance, tested by means of a simulation plan, is confirmed by a comparison with two other indices proposed in the literature, one of which is based on the Relative Risk of daily mortality, considering an application to real data.
A method for detecting malfunctions in PV solar panels based on electricity production monitoring
2017
In this paper a new method is developed for automatically detecting outliers or faults in the solar energy production of identical sets (sister arrays) of photovoltaic (PV) solar panels. The method involves a two-stage unsupervised approach. In the first stage, "in control" energy production data are created by using outlier detection methods and functional principal component analysis in order to remove global and local outliers from the data set. In the second stage, control charts for the "in control" data are constructed using both a parametric method and three non-parametric methods. The control charts can be used to detect outliers or faults in the production data in real-time or at t…
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…
Monitoring neonatal fungal infection with metabolomics
2014
Abstract The objective of our study was to evaluate the capability of the metabolomics approach to identify the variations of urine metabolites over time related to the neonatal fungal septic condition. The study population included a clinical case of a preterm neonate with invasive fungal infection and 13 healthy preterm controls. This study showed a unique urine metabolic profile of the patient affected by fungal sepsis compared to urine of controls and it was also possible to evaluate the efficacy of therapy in improving patient health.
WiWeHAR: Multimodal Human Activity Recognition Using Wi-Fi and Wearable Sensing Modalities
2020
Robust and accurate human activity recognition (HAR) systems are essential to many human-centric services within active assisted living and healthcare facilities. Traditional HAR systems mostly leverage a single sensing modality (e.g., either wearable, vision, or radio frequency sensing) combined with machine learning techniques to recognize human activities. Such unimodal HAR systems do not cope well with real-time changes in the environment. To overcome this limitation, new HAR systems that incorporate multiple sensing modalities are needed. Multiple diverse sensors can provide more accurate and complete information resulting in better recognition of the performed activities. This article…