Generalized Geographically Weighted Regression Model within a Modularized Bayesian Framework

Liu, Yang; Goudie, Robert J. B.

doi:10.48550/arxiv.2106.00996

Cited by 3 publications

(3 citation statements)

References 66 publications

(69 reference statements)

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“…Cut models have been applied in a broad range of areas, such as pharmacokinetic-pharmacodynamic data modeling [39,54,71], complex computer models [35] and epidemiology [44]. Cut and semi-modular models can be computationally challenging to fit, requiring nested MCMC by default [29], though there is currently much active work on developing more effective computationally strategies [30,37,54,55,70] as well as clarifying such models' technical properties [36,49,55].…”

Section: Structural Robustnessmentioning

confidence: 99%

Interoperability of Statistical Models in Pandemic Preparedness: Principles and Reality

et al. 2022

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We present interoperability as a guiding framework for statistical modelling to assist policy makers asking multiple questions using diverse datasets in the face of an evolving pandemic response. Interoperability provides an important set of principles for future pandemic preparedness, through the joint design and deployment of adaptable systems of statistical models for disease surveillance using probabilistic reasoning. We illustrate this through case studies for inferring and characterising spatial-temporal prevalence and reproduction numbers of SARS-CoV-2 infections in England.

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Section: Structural Robustnessmentioning

confidence: 99%

Interoperability of Statistical Models in Pandemic Preparedness: Principles and Reality

et al. 2022

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“…Semi-modular inference interpolates between cut and full posterior distributions continuously using a tuning parameter ω ∈ [0, 1], where ω = 0 corresponds to the cut posterior and ω = 1 to the full posterior. Further developments and applications are discussed in Liu and Goudie (2021), Carmona and Nicholls (2022), Nicholls et al (2022a) and Frazier and Nott (2022). The motivation for semi-modular inference is the existence of a bias-variance trade-off: as Carmona and Nicholls (2020) explain, "In Cut-model inference, feedback from the suspect module is completely cut.…”

Section: Introductionmentioning

confidence: 99%

Guaranteed Accuracy of Semi-Modular Posteriors

Frazier¹,

Nott²

2023

Preprint

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Bayesian inference has widely acknowledged advantages in many problems, but it can also be unreliable when the model is misspecified. Bayesian modular inference is concerned with complex models which have been specified through a collection of coupled submodels, and is useful when there is misspecification in some of the submodels. The submodels are often called modules in the literature. Cutting feedback is a widely used Bayesian modular inference method which ensures that information from suspect model components is not used in making inferences about parameters in correctly specified modules. However, it may be hard to decide in what circumstances this "cut posterior" is preferred to the exact posterior. When misspecification is not

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“…LeSage [29] suggested an early version of BGWR, where the prior distribution of the parameters depends on expert knowledge. More recent approaches have been proposed by Ma [37], who proposed BGWR based on the weighted log-likelihood, and Liu [34] proposed BGWR based on a weighted least-squares approach.…”

Section: Introductionmentioning

confidence: 99%

Spatial statistical machine learning models to assess the relationship between development vulnerabilities and educational factors in children in Queensland, Australia

et al. 2022

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Background The health and development of children during their first year of full time school is known to impact their social, emotional, and academic capabilities throughout and beyond early education. Physical health, motor development, social and emotional well-being, learning styles, language and communication, cognitive skills, and general knowledge are all considered to be important aspects of a child’s health and development. It is important for many organisations and governmental agencies to continually improve their understanding of the factors which determine or influence development vulnerabilities among children. This article studies the relationships between development vulnerabilities and educational factors among children in Queensland, Australia. Methods Spatial statistical machine learning models are reviewed and compared in the context of a study of geographic variation in the association between development vulnerabilities and attendance at preschool among children in Queensland, Australia. A new spatial random forest (SRF) model is suggested that can explain more of the spatial variation in data than other approaches. Results In the case study, spatial models were shown to provide a better fit compared to models that ignored the spatial variation in the data. The SRF model was shown to be the only model which can explain all of the spatial variation in each of the development vulnerabilities considered in the case study. The spatial analysis revealed that the attendance at preschool factor has a strong influence on the physical health domain vulnerability and emotional maturity vulnerability among children in their first year of school. Conclusion This study confirmed that it is important to take into account the spatial nature of data when fitting statistical machine learning models. A new spatial random forest model was introduced and was shown to explain more of the spatial variation and provide a better model fit in the case study of development vulnerabilities among children in Queensland. At small-area population level, increased attendance at preschool was strongly associated with reduced physical and emotional development vulnerabilities among children in their first year of school.

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Generalized Geographically Weighted Regression Model within a Modularized Bayesian Framework

Cited by 3 publications

References 66 publications

Interoperability of Statistical Models in Pandemic Preparedness: Principles and Reality

Interoperability of Statistical Models in Pandemic Preparedness: Principles and Reality

Guaranteed Accuracy of Semi-Modular Posteriors

Spatial statistical machine learning models to assess the relationship between development vulnerabilities and educational factors in children in Queensland, Australia

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