Search results for "clustered data"

showing 3 items of 3 documents

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
researchProduct

Multilevel Latent Profile Analysis With Covariates : Identifying Job Characteristics Profiles in Hierarchical Data as an Example

2018

Latent profile analysis (LPA) is a person-centered method commonly used in organizational research to identify homogeneous subpopulations of employees within a heterogeneous population. However, in the case of nested data structures, such as employees nested in work departments, multilevel techniques are needed. Multilevel LPA (MLPA) enables adequate modeling of subpopulations in hierarchical data sets. MLPA enables investigation of variability in the proportions of Level 1 profiles across Level 2 units, and of Level 2 latent classes based on the proportions of Level 1 latent profiles and Level 1 ratings, and the extent to which covariates drawn from the different hierarchical levels of the…

ominaisuudetmultilevel latent profile analysishierarchical structurejob demand-control-support modeltyöntekijätanalyysiclustered datatyöprofiilit (tieto)
researchProduct

Multilevel Latent Profile Analysis With Covariates : Identifying Job Characteristics Profiles in Hierarchical Data as an Example

2018

Latent profile analysis (LPA) is a person-centered method commonly used in organizational research to identify homogeneous subpopulations of employees within a heterogeneous population. However, in the case of nested data structures, such as employees nested in work departments, multilevel techniques are needed. Multilevel LPA (MLPA) enables adequate modeling of subpopulations in hierarchical data sets. MLPA enables investigation of variability in the proportions of Level 1 profiles across Level 2 units, and of Level 2 latent classes based on the proportions of Level 1 latent profiles and Level 1 ratings, and the extent to which covariates drawn from the different hierarchical levels of th…

multilevel latent profile analysisComputer scienceStrategy and ManagementGeneral Decision SciencestyöHierarchical database model0504 sociologyManagement of Technology and Innovation0502 economics and businessStatisticsCovariatetyöntekijätjob demand-control-support modelClustered dataclustered datata515Analysis of covarianceta11205 social sciences050401 social sciences methodsMixture modelprofiilit (tieto)Heterogeneous populationominaisuudetHomogeneoushierarchical structureanalyysiJob demand control support model050203 business & managementOrganizational Research Methods
researchProduct