Search results for "multivariate statistic"

showing 10 items of 327 documents

Archetypal analysis: contributions for estimating boundary cases in multivariate accommodation problem

2013

[EN] The use of archetypal analysis is proposed in order to determine a set of representative cases that entail a certain percentage of the population, in the accommodation problem. A well-known anthropometric database has been used in order to compare our methodology with the common used PCA-approach, showing the advantages of our methodology: the level of accommodation is reached unlike the PCA approach, no more adjustments are necessary, the user can decide the number of archetypes to consider or leave the selection by a criterion. Unlike PCA, the objective of the archetypal analysis is obtaining extreme individuals, so it is the appropriate statistical technique for solving this type of…

Multivariate statisticsrepresentative human model generationGeneral Computer ScienceComputer scienceBoundary (topology)Type (model theory)Anthropometry [Percentile]computer.software_genrearchetypepercentileSet (abstract data type)Archetypal analysisStatisticsArchetypeSelection (genetic algorithm)Archetypeanthropometryrepresentative casebusiness.industryGeneral EngineeringRepresentative humanPercentile: AnthropometryModel generationRepresentative caseData miningbusinesscomputerAccommodation
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Testing different methodologies for Granger causality estimation: A simulation study

2021

Granger causality (GC) is a method for determining whether and how two time series exert causal influences one over the other. As it is easy to implement through vector autoregressive (VAR) models and can be generalized to the multivariate case, GC has spread in many different areas of research such as neuroscience and network physiology. In its basic formulation, the computation of GC involves two different regressions, taking respectively into account the whole past history of the investigated multivariate time series (full model) and the past of all time series except the putatively causal time series (restricted model). However, the restricted model cannot be represented through a finit…

Multivariate statisticsstate space modelsSeries (mathematics)Computer scienceGranger causality; state space modelsDynamical NetworksMultivariate Time SeriesReduction (complexity)Autoregressive modelGranger causalitySettore ING-INF/06 - Bioingegneria Elettronica E InformaticaGranger causalityState spaceConditioningTime seriesVector Autoregressive ProcessesAlgorithm2020 28th European Signal Processing Conference (EUSIPCO)
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Vector Autoregressive Fractionally Integrated Models to Assess Multiscale Complexity in Cardiovascular and Respiratory Time Series

2020

Cardiovascular variability is the result of the activity of several physiological control mechanisms, which involve different variables and operate across multiple time scales encompassing short term dynamics and long range correlations. This study presents a new approach to assess the multiscale complexity of multivariate time series, based on linear parametric models incorporating autoregressive coefficients and fractional integration. The approach extends to the multivariate case recent works introducing a linear parametric representation of multiscale entropy, and is exploited to assess the complexity of cardiovascular and respiratory time series in healthy subjects studied during postu…

Multivariate statisticsvector autoregressive fractionally integrated (VARFI) modelComputer scienceQuantitative Biology::Tissues and OrgansPhysics::Medical Physicssystolic arterial pressure (SAP)Cardiovascular variabilitycomputer.software_genreCorrelationAutoregressive modelmultiscale entropy (MSE)heart period (HP)Settore ING-INF/06 - Bioingegneria Elettronica E InformaticaParametric modelMultiple timeEntropy (information theory)Data miningTime seriescomputerParametric statistics2020 11th Conference of the European Study Group on Cardiovascular Oscillations (ESGCO)
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Elliptically Symmetric Distributions: A Review of Achieved Results and Open Issues

2005

The spherically and elliptically symmetrical distributions are used in different statistical areas for different purposes such as the description of multivariate data, in order to find alternatives to the normal distribution in multinormality tests and in the creation of statistical models in which the usual assumption of normality is not realistic. Some achieved results, open issues and some proposals for their use in applications, especially in the financial area, are here presented.

Normal distributionMultivariate statisticsOrder (business)media_common.quotation_subjectStatisticsMultivariate normal distributionStatistical modelExcess returnNormalitymedia_commonMathematics
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Asymmetric semi-volatility spillover effects in EMU stock markets

2018

Abstract The aim of this paper is to quantify the strength and the direction of semi-volatility spillovers between five EMU stock markets over the 2000–2016 period. We use upside and downside semi-volatilities as proxies for downside risk and upside opportunities. In this way, we aim to complement the literature, which has focused mainly on the contemporaneous correlation between positive and negative returns, with the evidence of asymmetry also in semi-volatility transmission. For this purpose, we apply the Diebold and Yilmaz (2012) methodology, based on a generalized forecast error variance decomposition, to downside and upside realized semi-volatility series. While the analysis of Diebol…

Normalization (statistics)Multivariate statisticsEconomics and Econometrics050208 financeForecast error variance decomposition05 social sciencessemi-volatility asymmetry forecast error variance decompositionVolatility spilloverDownside riskSemi-volatilitySettore SECS-P/05 - EconometriaAsymmetryFull sampleSpilloverSpillover effect0502 economics and businessVHAREconometricsVariance decomposition of forecast errorsEconomicsSemi-volatility Asymmetry Forecast error variance decomposition Spillover VHAR050207 economicsStock (geology)FinanceInternational Review of Financial Analysis
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NMR spectroscopy evaluation of direct relationship between soils and molecular composition of red wines from Aglanico grapes

2010

1H NMR spectroscopy was employed to investigate the molecular quality of Aglianico red wines from the Campania region of Italy. The wines were obtained from three different Aglianico vineyards characterized by different microclimatic and pedological properties. In order to reach an objective evaluation of “terroir” influence on wine quality, grapes were subjected to the same winemaking procedures. The careful subtraction of water and ethanol signals from NMR spectra allowed to statistically recognize the metabolites to be employed in multivariate statistical methods: Principal Component Analysis (PCA), Discriminant Analysis (DA) and Hierarchical Clustering Analysis (HCA). The three wines we…

Nuclear Magnetic ResonanceSettore AGR/13 - Chimica AgrariaAnalytical chemistryMultivariate statistical analysiBiochemistryAnalytical ChemistryChemometricsEnvironmental ChemistryOrganic matterFood scienceSpectroscopyWinemakingTerroirWinechemistry.chemical_classificationterroirChemistrydigestive oral and skin physiologyfood and beveragesNuclear magnetic resonance spectroscopyNuclear Magnetic Resonance; Aglianico red wines; Multivariate statistical analysis; terroirAglianico red wineSoil waterPrincipal component analysisSettore AGR/16 - Microbiologia Agraria
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Multivariate exponential smoothing: A Bayesian forecast approach based on simulation

2009

This paper deals with the prediction of time series with correlated errors at each time point using a Bayesian forecast approach based on the multivariate Holt-Winters model. Assuming that each of the univariate time series comes from the univariate Holt-Winters model, all of them sharing a common structure, the multivariate Holt-Winters model can be formulated as a traditional multivariate regression model. This formulation facilitates obtaining the posterior distribution of the model parameters, which is not analytically tractable: simulation is needed. An acceptance sampling procedure is used in order to obtain a sample from this posterior distribution. Using Monte Carlo integration the …

Numerical AnalysisMultivariate statisticsGeneral Computer ScienceApplied MathematicsUnivariateMarkov chain Monte CarloTheoretical Computer ScienceNormal-Wishart distributionsymbols.namesakeUnivariate distributionModeling and SimulationStatisticssymbolsMultivariate t-distributionBayesian linear regressionGibbs samplingMathematicsMathematics and Computers in Simulation
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The Human Biodiversity in the Middle of the Mediterranean. Study of native and settlers populations on the Sicilian context

2020

[IT] Negli ultimi 200.000 anni, la specie umana si è diffusa in tutta la Terra, adattando la sua morfologia e fisiologia a un'ampia gamma di habitat. Lo scheletro umano ha quindi registrato i principali effetti ambientali e di conseguenza i reperti scheletrici assumono grande importanza nell'indagine dei processi evolutivi. Oggi le moderne tecniche di indagini quantitative delle principali caratteristiche morfologiche consentono di metterle in relazione con la variabilità genetica. La posizione geografica della Sicilia, l'isolamento e la sua lunga e dinamica storia di colonizzazione (diversi e numerosi contributi culturali e biologici) hanno creato un contesto peculiare che consente uno stu…

OdontometricsGeometric Moprhometrics Photogrammetry 3D Models Human skulls SicliyRange (biology)PopulationBiodiversityContext (language use)Settore BIO/08 - AntropologiaStatistiche multivariateAntropologia fisicaMorfometria geometricaFotogrametriaGenetic variabilityeducationEvoluzione umanaSicilyHuman evolutioneducation.field_of_studyGeometric morphometricsEvolución humanaMorfometría geométricaHuman migrationbusiness.industryEcologyFotogrammetrialanguage.human_languagePhysical anthropologyMultivariate statisticsGeographyPhotogrammetrylanguageSiciliaEstadísticas multivariadasAntropología físicabusinessMATEMATICA APLICADASicilian
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Revised risk estimation and treatment stratification of low- and intermediate-risk neuroblastoma patients by integrating clinical and molecular progn…

2014

Abstract Purpose: To optimize neuroblastoma treatment stratification, we aimed at developing a novel risk estimation system by integrating gene expression–based classification and established prognostic markers. Experimental Design: Gene expression profiles were generated from 709 neuroblastoma specimens using customized 4 × 44 K microarrays. Classification models were built using 75 tumors with contrasting courses of disease. Validation was performed in an independent test set (n = 634) by Kaplan–Meier estimates and Cox regression analyses. Results: The best-performing classifier predicted patient outcome with an accuracy of 0.95 (sensitivity, 0.93; specificity, 0.97) in the validation coh…

OncologyMaleCancer ResearchMultivariate statisticsmedicine.medical_specialtyKaplan-Meier EstimateBioinformaticsRisk AssessmentNeuroblastomaText miningRisk FactorsInternal medicineNeuroblastomamedicineBiomarkers TumorCluster AnalysisHumansbusiness.industryProportional hazards modelGene Expression ProfilingReproducibility of ResultsRegression analysismedicine.diseasePrognosisClinical trialGene expression profilingGene Expression Regulation NeoplasticOncologyRegression AnalysisFemalebusinessRisk assessmentFollow-Up StudiesClinical cancer research : an official journal of the American Association for Cancer Research
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Multivariate independent prognostic factors in endometrial carcinoma: A clinicopathologic study in 181 patients

2003

The aim of this study was to evaluate the biologic outcome of endometrial carcinomas as compared to clinical and pathologic parameters and to identify multivariate independent prognostic factors. Charts were abstracted from patients with endometrial carcinoma from 1985 to 1995. Data on clinicopathologic variables, adjuvant treatment, site of recurrence, and survival were collected. χ2 test was used to test association between variables. Kaplan-Maier method was used for survival analysis and Cox proportional hazards model for multiple regression analysis. Univariate analysis revealed that FIGO stage, tumor grade, depth of myometrial invasion, biochemical analysis of progesterone receptor sta…

OncologyMultivariate statisticsUnivariate analysismedicine.medical_specialtyMultivariate analysisbusiness.industryProportional hazards modelObstetrics and GynecologyProgesterone Receptor Statusmedicine.diseaseOncologyInternal medicineCarcinomamedicineStage (cooking)businessSurvival analysisInternational Journal of Gynecologic Cancer
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