Search results for "GCV"

showing 2 items of 2 documents

Impact of preclinical carotid atherosclerosis on global cardiovascular risk stratification and events in a 10-year follow-up: comparison between the …

2019

Background The aim of the study was to compare three widely used algorithms for stratification of the global cardiovascular risk (GCVR): the Framingham Heart Study (FHS) score, the European systemic coronary risk estimation (SCORE) and the Italian 'Progetto Cuore' (heart project) score. It was also investigated how preclinical carotid atherosclerosis (pre-ATS) might influence the incidence and improve the risk prediction of cerebrovascular and cardiovascular events. Methods Subjects (n = 358) without previous history of cardiovascular disease (CVD) were recruited and the GCVR was calculated for each patient. An ultrasound evaluation of the carotid arteries was also performed. Results Accord…

Carotid Artery DiseasesMaleTime Factorsintima-media thickening (IMT)Predictive Value of TestDisease030204 cardiovascular system & hematologyCarotid Intima-Media ThicknessDecision Support Technique0302 clinical medicineFramingham Heart StudyRisk FactorsProspective Studies030212 general & internal medicineProspective cohort studyasymptomatic carotid plaque (ACP)education.field_of_studyIncidence (epidemiology)IncidenceGeneral MedicineMiddle AgedPrognosisPlaque AtheroscleroticAlgorithmItalypreclinical carotid atherosclerosis (pre-ATS)Predictive value of testsCerebrovascular DisorderDisease ProgressionFemalemedicine.symptomRisk assessmentCardiology and Cardiovascular MedicineAlgorithmAlgorithmsHumanAdultTime FactorPrognosiPopulationglobal cardiovascular risk (GCVR)AsymptomaticRisk AssessmentDecision Support TechniquesFollow-Up Studie03 medical and health sciencesPredictive Value of TestsCarotid Intima-Media ThicknemedicineHumanseducationAgedAsymptomatic DiseaseCarotid Artery Diseasebusiness.industryRisk Factoralgorithms of cardiovascular riskCerebrovascular DisordersProspective StudieAsymptomatic DiseasesbusinessFollow-Up Studies
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Tuning parameter selection in LASSO regression

2016

We propose a new method to select the tuning parameter in lasso regression. Unlike the previous proposals, the method is iterative and thus it is particularly efficient when multiple tuning parameters have to be selected. The method also applies to more general regression frameworks, such as generalized linear models with non-normal responses. Simulation studies show our proposal performs well, and most of times, better when compared with the traditional Bayesian Information Criterion and Cross validation.

GCVBICSchall algorithmtuning parameter selection; lasso; GCV; BIC; CV; Schall algorithmtuning parameter selectionCVlassoSettore SECS-S/01 - Statistica
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