Search results for "Mixed Mode"

showing 10 items of 72 documents

Estimating COVID-19-induced Excess Mortality in Lombardy

2021

AbstractWe compare the expected all-cause mortality with the observed one for different age classes during the pandemic in Lombardy, which was the epicenter of the epidemic in Italy and still is the region most affected by the pandemic. A generalized linear mixed model is introduced to model weekly mortality from 2011 to 2019, taking into account seasonal patterns and year-specific trends. Based on the 2019 year-specific conditional best linear unbiased predictions, a significant excess of mortality is estimated in 2020, leading to approximately 35000 more deaths than expected, mainly arising during the first wave. In 2021, instead, the excess mortality is not significantly different from z…

Excess mortality2019-20 coronavirus outbreakCoronavirus disease 2019 (COVID-19)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)PandemicStatisticsBiologyGeneralized linear mixed model
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A Critical Review of Statistical Methods for Twin Studies Relating Exposure to Early Life Health Conditions

2021

International audience; When investigating disease etiology, twin data provide a unique opportunity to control for confounding and disentangling the role of the human genome and exposome. However, using appropriate statistical methods is fundamental for exploiting such potential. We aimed to critically review the statistical approaches used in twin studies relating exposure to early life health conditions. We searched PubMed, Scopus, Web of Science, and Embase (2011–2021). We identified 32 studies and nine classes of methods. Five were conditional approaches (within-pair analyses): additive-common-erratic (ACE) models (11 studies), generalized linear mixed models (GLMMs, five studies), gene…

ExposomeComputer scienceHealth Toxicology and MutagenesisInferenceMarginal modelReviewexposomeGeneralized linear mixed modeltwin data03 medical and health sciences0302 clinical medicineDiscriminative modelchildren[STAT.AP] Statistics [stat]/Applications [stat.AP]StatisticsHumans030212 general & internal medicineGeneralized estimating equationchildren Exposome Genome Health Statistical methods Twin data Humans Linear Models Models Statisticalgenome030304 developmental biology0303 health sciences[STAT.AP]Statistics [stat]/Applications [stat.AP]Models StatisticalConfoundingPublic Health Environmental and Occupational HealthRhealthTwin studychildren exposome genome health statistical methods twin data[SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologieLinear Modelsstatistical methodsMedicine[SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie
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“You look at it, but will you choose it”: Is there a link between the foods consumers look at and what they ultimately choose in a virtual supermarke…

2022

Most of the studies that showed a link between gaze allocation and consumer's food choices were performed on food products belonging to a same category. However, consumers usually make food choices in more complex environments, between many different products, and different factors can influence their choices. Therefore, our study aimed to understand the link between gaze behavior and food choices in a complex and realistic situation of choice. Participants (n=99) performed a food choice task in a virtual supermarket. They had to choose three food products to create a dish in four scenarios evoking different motivations (focus on health, environment, food pleasure, and daily scenario as con…

Eye trackingNutrition and Dieteticsconsumerfood choiceconsumers[SHS]Humanities and Social Sciencesfood motivationsmeat[SDV.AEN] Life Sciences [q-bio]/Food and Nutritionfood choicesgeneralized linear mixed model (GLMM)virtual supermarketpulsesgaze behavior[SDV.AEN]Life Sciences [q-bio]/Food and Nutritionvirtual reality (VR)Food Science
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An ensemble approach to short-term forecast of COVID-19 intensive care occupancy in Italian Regions

2020

Abstract The availability of intensive care beds during the COVID‐19 epidemic is crucial to guarantee the best possible treatment to severely affected patients. In this work we show a simple strategy for short‐term prediction of COVID‐19 intensive care unit (ICU) beds, that has proved very effective during the Italian outbreak in February to May 2020. Our approach is based on an optimal ensemble of two simple methods: a generalized linear mixed regression model, which pools information over different areas, and an area‐specific nonstationary integer autoregressive methodology. Optimal weights are estimated using a leave‐last‐out rationale. The approach has been set up and validated during t…

FOS: Computer and information sciencesStatistics and ProbabilityTime FactorsOccupancyCoronavirus disease 2019 (COVID-19)Computer science01 natural sciencesGeneralized linear mixed modelSARS‐CoV‐2law.inventionclustered data; COVID-19; generalized linear mixed model; integer autoregressive; integer autoregressive model; panel data; SARS-CoV-2; weighted ensembleMethodology (stat.ME)panel data010104 statistics & probability03 medical and health sciences0302 clinical medicinelawCOVID‐19Intensive careEconometricsHumansclustered data030212 general & internal medicine0101 mathematicsPandemicsStatistics - MethodologySARS-CoV-2Reproducibility of ResultsCOVID-19General Medicineweighted ensembleIntensive care unitResearch PapersTerm (time)integer autoregressiveIntensive Care UnitsAutoregressive modelItalyNonlinear Dynamicsgeneralized linear mixed modelinteger autoregressive modelclustered data; COVID-19; generalized linear mixed model; integer autoregressive; integer autoregressive model; panel data; SARS-CoV-2; weighted ensemble; COVID-19; Humans; Intensive Care Units; Italy; Nonlinear Dynamics; Pandemics; Reproducibility of Results; Time Factors; ForecastingStatistics Probability and UncertaintySettore SECS-S/01Settore SECS-S/01 - StatisticaPanel dataResearch PaperForecasting
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Micro damage and cracking in fibre reinforced composites by a novel hybrid numerical technique

2020

Article number 0033974 AIP Incluida en Conference Proceedings 2309 The prediction of failure mechanisms in fibre-reinforced composite materials is of great importance for the design of composite engineering applications. With the aim of providing a tool able to predict and explain the initiation and propagation of damage in unidirectional fiber reinforced composites, in this contribution we develop a micromechanical numerical model based on a novel hybrid approach coupling the virtual element method (VEM) and the boundary element method (BEM). The BEM is a popular numerical technique, efficient and accurate, which has been successfully applied to interfacial fracture mechanics problems of f…

Fiber Reinforced Composites Micro-mechanics Fracture-Mechanics Virtual Element MethodMixed ModeComputer scienceComposite numberFiber-reinforced compositeFinite element methodMatrix (mathematics)Fracture (geology)Coupling (piping)Polygon meshPolycrystalline MaterialsComposite materialSettore ING-IND/04 - Costruzioni E Strutture AerospazialiBoundary element methodCohesive Zone Model
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An analysis of interface delamination mechanisms in orthotropic and hybrid fiber-metal composite laminates

2007

Abstract The onset and propagation of interlaminar defects is one of the main damage mechanisms in composite materials. This is even more the case when considering layered materials comprising metallic laminae (typically Aluminium) and FRP laminae. Propagation of delamination mainly depends on the initial crack extension and its loading mode. This work presents some results of a combined analytical–numerical–experimental study on the onset and propagation mechanisms regarding interlaminar defects. Two cases have been analysed in particular, the first consisting of a glass-fibre reinforced epoxy resin laminate, and the second consisting of a hybrid laminate where a lamina of aluminium is lay…

Fiber metal laminateMaterials scienceMechanical EngineeringDelaminationComposite materialsFracture toughnessDissimilar mixed mode bending specimenchemistry.chemical_elementFracture mechanicsEpoxyFibre-reinforced plasticComposite laminatesOrthotropic materialSettore ING-IND/14 - Progettazione Meccanica E Costruzione Di MacchinechemistryMechanics of MaterialsAluminiumvisual_artvisual_art.visual_art_mediumGeneral Materials ScienceComposite materialEngineering Fracture Mechanics
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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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A Widrow–Hoff Learning Rule for a Generalization of the Linear Auto-associator

1996

Abstract A generalization of the linear auto-associator that allows for differential importance and nonindependence of both the stimuli and the units has been described previously by Abdi (1988). This model was shown to implement the general linear model of multivariate statistics. In this note, a proof is given that the Widrow–Hoff learning rule can be similarly generalized and that the weight matrix will converge to a generalized pseudo-inverse when the learning parameter is properly chosen. The value of the learning parameter is shown to be dependent only upon the (generalized) eigenvalues of the weight matrix and not upon the eigenvectors themselves. This proof provides a unified framew…

General linear modelArtificial neural networkbusiness.industryGeneralizationApplied MathematicsGeneralized linear array modelMachine learningcomputer.software_genreGeneralized linear mixed modelHierarchical generalized linear modelLearning ruleApplied mathematicsArtificial intelligencebusinesscomputerGeneral PsychologyEigenvalues and eigenvectorsMathematicsJournal of Mathematical Psychology
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Dealing with the Pseudo-Replication Problem in Longitudinal Data from Posidonia Oceanica Surveys: Modeling Dependence vs. Subsampling

2012

Posidonia oceanica represents the key species of the most important ecosystem in subtidal habitats of the Mediterranean Sea. Being sensitive to changes in the environment, it is considered a crucial indicator of the quality of coastal marine waters. A peculiarity of P. oceanica is the presence of reiterative modules characterizing its growth, which lend themselves to back-dating techniques, allowing for the reconstruction of past history of growth variables (annual rhizome elongation and diameter, primary production, etc.). Such back-dating techniques provide, for each sampled shoot, a longitudinal series of multivariate data; this is an instance of what Hurlbert (1984) in a seminal paper d…

Generalized linear mixed modelSub-samplingPseudo-replicationMarine ecolology
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Model averaging estimation of generalized linear models with imputed covariates

2015

a b s t r a c t We address the problem of estimating generalized linear models when some covariate values are missing but imputations are available to fill-in the missing values. This situation generates a bias-precision trade- off in the estimation of the model parameters. Extending the generalized missing-indicator method proposed by Dardanoni et al. (2011) for linear regression, we handle this trade-off as a problem of model uncertainty using Bayesian averaging of classical maximum likelihood estimators (BAML). We also propose a block model averaging strategy that incorporates information on the missing-data patterns and is computationally simple. An empirical application illustrates our…

Generalized linear modelEconomics and EconometricsApplied MathematicsSettore SECS-P/05 - EconometriaEstimatorMissing dataGeneralized linear mixed modelModel averaging Bayesian averaging of maximum likelihood destimators Generalized linear models Missing covariates Generalized missing-indicator method shareHierarchical generalized linear modelStatisticsLinear regressionCovariateApplied mathematicsGeneralized estimating equationMathematics
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