Search results for "random effects"
showing 5 items of 55 documents
Bayesian Analysis of Population Health Data
2021
The analysis of population-wide datasets can provide insight on the health status of large populations so that public health officials can make data-driven decisions. The analysis of such datasets often requires highly parameterized models with different types of fixed and random effects to account for risk factors, spatial and temporal variations, multilevel effects and other sources on uncertainty. To illustrate the potential of Bayesian hierarchical models, a dataset of about 500,000 inhabitants released by the Polish National Health Fund containing information about ischemic stroke incidence for a 2-year period is analyzed using different types of models. Spatial logistic regression and…
Lifelong Residential Exposure to Green Space and Attention: A Population-based Prospective Study
2017
C.T. is a recipient of a European Respiratory Society Fellowship (RESPIRE2–2015–7251) P.D. is funded by a Ramón y Cajal fellowship (RYC-2012-10995) awarded by the Spanish Ministry of Economy and Competitiveness. S.L. is funded by a Miguel Servet-FEDER fellowship (MS15/0025) awarded by the Spanish Ministry of Economy and Competitiveness. M.G. is funded by a Miguel Servet-FEDER fellowship (MS13/00054) awarded by the Spanish Ministry of Economy and Competitiveness
Bayesian longitudinal models for paediatric kidney transplant recipients
2015
Chronic kidney disease is a progressive loss of renal function which results in the inability of the kidneys to properly filter waste from the blood. Renal function is usually estimated by the glomerular filtration rate (eGFR), which decreases with the worsening of the disease. Bayesian longitudinal models with covariates, random effects, serial correlation and measurement error are discussed to analyse the progression of eGFR in first transplanted children taken from a study in Valencia, Spain.
Dynamics of female labour force participation in France
2013
International audience; This article formulates and estimates a structural intertemporal model of labour force participation. Relying on theoretical characterizations derived from an economic model of lifetime behaviour, we estimate a dynamic probit model with correlated random effects using longitudinal data to allow for a dynamic structure. The model is applied to a panel of married women drawn from the 1997–2002 French Labour Force surveys in order to represent their participation behaviour. It is estimated by maximum simulated likelihood. Our results show that women’s decisions to go out to work are characterized by significant state dependence, unobserved heterogeneity and negative ser…
Varying-time random effects models for longitudinal data: unmixing and temporal interpolation of remote-sensing data
2008
Remote sensing is a helpful tool for crop monitoring or vegetation-growth estimation at a country or regional scale. However, satellite images generally have to cope with a compromise between the time frequency of observations and their resolution (i.e. pixel size). When concerned with high temporal resolution, we have to work with information on the basis of kilometric pixels, named mixed pixels, that represent aggregated responses of multiple land cover. Disaggreggation or unmixing is then necessary to downscale from the square kilometer to the local dynamic of each theme (crop, wood, meadows, etc.). Assuming the land use is known, that is to say the proportion of each theme within each m…