Results 31 to 40 of about 3,561,036 (197)
BMC Public Health, 2022 Background Overweight and obesity are one of the most significant risk factors of the twenty-first century related to an increased risk in the occurrence of non-communicable diseases and associated increased healthcare costs.
Robby De Pauw +5 more
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A review of R-packages for random-intercept probit regression in small clusters
Frontiers in Applied Mathematics and Statistics, 2016 Generalized Linear Mixed Models (GLMMs) are widely used to model clustered categorical outcomes. To tackle the intractable integration over the random effects distributions, several approximation approaches have been developed for likelihood-based ...
Haeike Josephy, Tom Loeys, Yves Rosseel
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Mathematics, 2023 Geostatistics data in regions always have highly spatial heterogeneous, yet the regional features of the data itself cannot be ignored. In this paper, a novel latent Bayesian spatial model is proposed, which incorporates the spatial dependence of ...
Zongyuan Xia +4 more
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Approximate Bayesian Inference for Latent Gaussian models by using Integrated Nested Laplace Approximations [PDF]
Journal of the Royal Statistical Society Series B: Statistical Methodology, 2009 Summary Structured additive regression models are perhaps the most commonly used class of models in statistical applications. It includes, among others, (generalized) linear models, (generalized) additive models, smoothing spline models, state space models, semiparametric regression, spatial and spatiotemporal models, log-Gaussian Cox
Rue, Havard +2 more
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Incorporating Biotic Information in Species Distribution Models: A Coregionalized Approach
Mathematics, 2021 In this work, we discuss the use of a methodological approach for modelling spatial relationships among species by means of a Bayesian spatial coregionalized model.
Xavier Barber +5 more
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Bayesian Analysis of Population Health Data
Mathematics, 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
Dorota Młynarczyk +3 more
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Spatial Bayesian Hierarchical Modelling with Integrated Nested Laplace Approximation
, 2020 We consider latent Gaussian fields for modelling spatial dependence in the context of both spatial point patterns and areal data, providing two different applications. The inhomogeneous Log-Gaussian Cox Process model is specified to describe a seismic sequence occurred in Greece, resorting to the Stochastic Partial Differential Equations.
D'Angelo N, Abbruzzo A, Adelfio G.
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Media Statistika, 2019 Poverty is a complex and multidimensional problem so that it becomes a development priority. Applications of poverty modeling in discrete data are still few and applications of the Bayesian paradigm are also still few.
Retsi Firda Maulina +2 more
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BMC Women's Health, 2023 Background Cervical cancer is the 4th most common cancer in women worldwide. as well as the 4th most common cause of cancer-related death. The main objective of this study was to identify factors that affect the survival time of outpatients with cervical
Chalachew Gashu +3 more
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EpiMix: A novel method to estimate effective reproduction number
Infectious Disease Modelling, 2023 Transmission potential of a pathogen, often quantified by the time-varying reproduction number Rt, provides the current pace of infection for a disease and indicates whether an emerging epidemic is under control. In this study, we proposed a novel method,
Shihui Jin +3 more
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