Search results for "component"

showing 10 items of 1682 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.).

Functional principal component analysisMixed modelVariable (computer science)CovariateEconometricsFunctional data analysisContext (language use)Sample (statistics)Nonparametric regressionMathematics
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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.

Functional principal component analysisPointwiseDynamic time warpingComputer sciencebusiness.industryFunctional data analysisPattern recognitionMathematical morphologyMeasure (mathematics)ToolboxComputingMethodologies_PATTERNRECOGNITIONArtificial intelligenceCluster analysisbusiness
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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.

Functional principal component analysisPollutantHuman healthIndex (economics)Aggregate (data warehouse)StatisticsAir pollutionmedicineEnvironmental scienceAggregate AQI standardizing transformation Relative Risk relative variabilitymedicine.disease_causeAir quality indexHealth impact assessment
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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…

Functional principal component analysisRenewable Energy Sustainability and the EnvironmentComputer sciencebusiness.industryElectricity production monitoring020209 energyPhotovoltaic systemStatistical quality control02 engineering and technologyAnomaly detectionStatistical process controlcomputer.software_genreSolar energyOutlier0202 electrical engineering electronic engineering information engineeringGeneral Materials ScienceControl chartAnomaly detectionData miningbusinesscomputerPhotovoltaic solar panel sister arraysParametric statistics
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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 analysisSmoothing splineComputer scienceEconometricsFunctional data analysisFunction (mathematics)Conditional probability distributionSettore SECS-S/01 - StatisticaConditional expectationFPCA conditional quantile functions conditional mean functionQuantile regressionQuantile
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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…

Functional principal component analysisStatistics and ProbabilityMultivariate statistics2300GeneralizationDimensionality reductionGeneralized additive modelFunctional data analysisFunctional principal component analysiContext (language use)computer.software_genreMultivariate spatio-temporal dataCovariateP-splineData miningStatistics Probability and UncertaintycomputerSmoothingGeneral Environmental ScienceMathematics
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Functional Linear Models for the Analysis of Similarity of Waveforms

2023

In seismology methods based on waveform similarity analysis are adopted to identify sequences of events characterized by similar fault mechanism and propagation pattern. Seismic waves can be considered as spatially interdependent, three dimensional curves depending on time and the waveform similarity analysis can be configured as a functional clustering approach, on the basis of which the membership is assessed by the shape of the temporal patterns. For providing qualitative extraction of the most important information from the recorded signals, we propose the use of metadata, related to the waves, as covariates of a functional response regression model. The temporal patterns of this effect…

Functional response regressionStructured functional principal componentFunctional data depthWaveforms clusteringSettore SECS-S/01 - Statistica
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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.

Fungal infectionmedicine.medical_specialtyUrineInfant Newborn DiseasessepsisSepsisSettore MED/38 - Pediatria Generale E SpecialisticaMetabolomicsIntensive Care Units NeonatalmedicineHumansMetabolomicsIntensive care medicinePrincipal Component AnalysisPatient affectedbusiness.industryInfant NewbornObstetrics and Gynecologymedicine.diseaseItalyMycosesfungal infectionsPediatrics Perinatology and Child HealthMetabolomePopulation studyMetabolomic; fungal infections; PrematurityFungal sepsisClinical caseneonatePrematuritybusinessBiomarkersMetabolic profilemetabolomicThe Journal of Maternal-Fetal & Neonatal Medicine
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Spatiotemporal and gender-specific parasitism in two species of gobiid fish

2018

Parasitism is considered a major selective force in natural host populations. Infections can decrease host condition and vigour, and potentially influence, for example, host population dynamics and behavior such as mate choice. We studied parasite infections of two common marine fish species, the sand goby (Pomatoschistus minutus) and the common goby (Pomatoschistus microps), in the brackish water Northern Baltic Sea. We were particularly interested in the occurrence of parasite taxa located in central sensory organs, such as eyes, potentially affecting fish behavior and mate choice. We found that both fish species harbored parasite communities dominated by taxa transmitted to fish through …

GOBY POMATOSCHISTUS-MINUTUSEYE FLUKECOMPONENT COMMUNITYvirulenssiparasitismiisäntälajithost-parasite interactionSEXUAL SELECTIONvirulenceMATE CHOICESAND GOBYCATARACT FORMATIONsukupuolivalintaparinvalintaloiset1181 Ecology evolutionary biologyGASTEROSTEUS-ACULEATUSparasite communitySPATIAL VARIATION3-SPINED STICKLEBACKSmate choice
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Sex Determination by Genomic Dot Blot Hybridization and HLA DQα Typing by PCR from Fixed Tissues

1992

Recent advances in molecular biology methods have significantly increased the ability to detect genetic variation at the genomic level for forensic purposes. However, the quality requirements for blood, fresh or frozen tissue as a source of DNA are a practical limitation for typing the victim in order to conduct investigations on unsolved cases. Since paraffin embedded specimens are easily obtainable the ability to study this material would be of great value in current forensic practice.

GROUP-SPECIFIC COMPONENTGenetic variationDot blotComputational biologyTypingFrozen tissueMOLECULAR BIOLOGY METHODSBiologyMolecular biologyParaffin embedded
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