Search results for "Multiple factor analysis"

showing 6 items of 6 documents

Associations between food consumption patterns and saliva composition: Specificities of eating difficulties children

2017

Identifying objective markers of dietwould be beneficial to research fields such as nutritional epidemiology. As a preliminary study on the validity of using saliva for this purpose, and in order to explore the relationship between saliva and diet, we focused on clearly contrasted groups of children: children with eating difficulties (ED) receiving at least 50% of their energy intake through artificial nutrition vs healthy controls (C). Saliva of ED and C children was analyzed by various methods (targeted biochemical analyses, 2-D electrophoresis coupled to MS, 1H NMR) and their diet was characterized using food frequency questionnaires, considering 148 food items grouped into 13 categories…

0301 basic medicineMaleSaliva[SDV]Life Sciences [q-bio]carbonic anhydraseBehavioral NeuroscienceTandem Mass Spectrometryalimentation de l'enfantSurveys and Questionnaireshuman feedingAmylaseFood scienceChildprotéomeCarbonic Anhydrases2. Zero hungerbiologycomportement alimentaireHaptoglobinFood selectivitysalivationChild Preschoolfood consumptionFemalealimentation humaineconsommation alimentaireExperimental and Cognitive Psychologyfood habitsFeeding and Eating Disorders03 medical and health sciencesFood Preferencessalivary biomarkerscomposition de la saliveMultiple factor analysisHumansSalivaanhydrase carboniquemétabolome[ SDV ] Life Sciences [q-bio]Nutritional epidemiologyFood Consumption PatternsFeeding Behaviordietary behavior030104 developmental biologySaliva compositionproteome Metabolome Salivary biomarkersSpectrometry Mass Matrix-Assisted Laser Desorption-Ionizationbiology.proteinMuramidaseEnergy Intakedietsécrétion salivaire
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Multiple factor analysis: principal component analysis for multitable and multiblock data sets

2013

multiple factor analysis barycentric discriminant analysis (mufabada)statis[STAT.TH] Statistics [stat]/Statistics Theory [stat.TH]principal component analysismultiblock correspondence analysisbarycentric discriminant analysis (bada)generalized procrustes analysis (gpa)[STAT.TH]Statistics [stat]/Statistics Theory [stat.TH][ STAT.TH ] Statistics [stat]/Statistics Theory [stat.TH]consensus pca[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST]multiblock pcamultiple factor analysis (mfa)multiple factorial analysis[ MATH.MATH-ST ] Mathematics [math]/Statistics [math.ST]multitable pca[MATH.MATH-ST] Mathematics [math]/Statistics [math.ST]indscalmultiblock barycentric discriminant analysis (mudica)generalized singular value decomposition
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Multiple factor analysis: principal component analysis for multitable and multiblock data sets

2013

Multiple factor analysis MFA, also called multiple factorial analysis is an extension of principal component analysis PCA tailored to handle multiple data tables that measure sets of variables coll...

Statistics and ProbabilityMeasure (data warehouse)business.industryPattern recognitionMultiple dataMultiple correspondence analysisRelationship squareMultiple factor analysisPrincipal component analysisArtificial intelligenceFactorial analysisGeneralized singular value decompositionbusinessMathematicsWiley Interdisciplinary Reviews: Computational Statistics
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Beyond GDP: an analysis of the socio-economic diversity of European regions

2019

International audience; This paper aims to analyze the socioeconomic diversity of the European Union (EU-28) regions from a dynamic perspective. For that purpose, we combine a series of exploratory space-time analysis approaches to multiple Factor Analysis (MFA) applied to a large range of indicators collected at the NUTS-2 level for the period 2000–2015 for the EU-28. First, we find that the first factor of MFA, interpreted as economic development (ECO-DEV), is spatially clustered and that a moderate convergence process is at work between European regions from 2000 to 2015. Second, when comparing these results with those obtained for Gross Domestic Product (GDP) per capita, we show that th…

VolkswirtschaftstheorieinequalityEconomics[SDV]Life Sciences [q-bio]UngleichheitRaumplanung und RegionalforschungEconomicsEconomic geographysozioökonomische Lage050207 economicsddc:710media_commoneconomic development (on national level)Städtebau Raumplanung Landschaftsgestaltung050208 finance05 social sciencesArea Development Planning Regional Research109001. No povertyWirtschaftsocioeconomic positionRegionalpolitik[SHS.ECO]Humanities and Social Sciences/Economics and Finance[SDV] Life Sciences [q-bio]Multiple factor analysis8. Economic growthExploratory space-time analysisBildungsniveauregional policySpatial autocorrelationNational EconomyEconomics and Econometricsmedia_common.quotation_subjectEuropean regionsArbeitsmarktWirtschaftsentwicklunglevel of education0502 economics and businessMultiple factor analysisddc:330media_common.cataloged_instanceMessungEuropean unionDatengewinnungBeyond gdpSpatial analysisSocioeconomic statusspatial autocorrelation; European regions; exploratory space-time analysis; multiple factor analysisLandscaping and area planningBevölkerungsentwicklungPerspective (graphical)EU policypopulation developmentregional differencedata captureregionaler UnterschiedEU-Politikmeasurementlabor marketEUDiversity (politics)Applied Economics
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STATIS and DISTATIS: optimum multitable principal component analysis and three way metric multidimensional scaling

2012

STATIS is an extension of principal component analysis PCA tailored to handle multiple data tables that measure sets of variables collected on the same observations, or, alternatively, as in a variant called dual-STATIS, multiple data tables where the same variables are measured on different sets of observations. STATIS proceeds in two steps: First it analyzes the between data table similarity structure and derives from this analysis an optimal set of weights that are used to compute a linear combination of the data tables called the compromise that best represents the information common to the different data tables; Second, the PCA of this compromise gives an optimal map of the observation…

Statistics and ProbabilityMathematical optimizationSimilarity (geometry)[STAT.TH]Statistics [stat]/Statistics Theory [stat.TH]Linear discriminant analysiscomputer.software_genre01 natural sciences[ STAT.TH ] Statistics [stat]/Statistics Theory [stat.TH]Correspondence analysisSet (abstract data type)010104 statistics & probability03 medical and health sciences0302 clinical medicine[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST]Multiple factor analysisPrincipal component analysisMetric (mathematics)Data miningMultidimensional scaling[ MATH.MATH-ST ] Mathematics [math]/Statistics [math.ST]0101 mathematicscomputer030217 neurology & neurosurgeryComputingMilieux_MISCELLANEOUSMathematics
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Typology of exogenous organic matters based on chemical and biochemical composition to predict potential nitrogen mineralization

2010

Our aim was to develop a typology predicting potential N availability of exogenous organic matters (EOMs) in soil based on their chemical characteristics. A database of 273 EOMs was constructed including analytical data of biochemical fractionation, organic C and N, and results of N mineralization during incubation of soil–EOM mixtures in controlled conditions. Multiple factor analysis and hierarchical classification were performed to gather EOMs with similar composition and N mineralization behavior. A typology was then defined using composition criteria to predict potential N mineralization. Six classes of EOM potential N mineralization in soil were defined, from high potential N minerali…

[SDV.BIO]Life Sciences [q-bio]/Biotechnologygenetic structures010501 environmental sciences01 natural sciencesMinéralisationBiochemical compositionOrganic ChemicalsWaste Management and DisposalHigh potentialhttp://aims.fao.org/aos/agrovoc/c_35657chemistry.chemical_classificationMineralsChemistry04 agricultural and veterinary sciencesGeneral MedicineComposition chimiqueClassificationhierarchical classificationDisponibilité d'élément nutritifCycle de l'azoteEnvironmental chemistryhttp://aims.fao.org/aos/agrovoc/c_5193http://aims.fao.org/aos/agrovoc/c_1794AlgorithmsP33 - Chimie et physique du solBiochemical fractionationEnvironmental EngineeringNitrogenhttp://aims.fao.org/aos/agrovoc/c_7170Mineralogybiochemical fractionationBioengineeringhttp://aims.fao.org/aos/agrovoc/c_27938FractionationTeneur en azoten mineralizationMatière organique du solhttp://aims.fao.org/aos/agrovoc/c_5268Fertilité du solMultiple factor analysisOrganic matterComputer SimulationNitrogen cycle0105 earth and related environmental sciencesRenewable Energy Sustainability and the EnvironmentP35 - Fertilité du sol[ SDV.BIO ] Life Sciences [q-bio]/BiotechnologyMineralization (soil science)eye diseasesAmendement organiqueModels Chemical040103 agronomy & agriculture0401 agriculture forestry and fisheriessense organsexogenous organic mattertypologyhttp://aims.fao.org/aos/agrovoc/c_12965http://aims.fao.org/aos/agrovoc/c_1653http://aims.fao.org/aos/agrovoc/c_15999F04 - Fertilisation
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