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Spatial non-parametric Bayesian clustered coefficients

delete2024-04-27
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OA
AI
W
Wala Draidi Areed *
A
Aiden Price
H
Helen Thompson
R
Reid Malseed
K
Kerrie Mengersen
DOI：10.1038/s41598-024-59973-wdelete
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Abstract

Abstract

En 中文
In the field of population health research, understanding the similarities between geographical areas and quantifying their shared effects on health outcomes is crucial. In this paper, we synthesise a number of existing methods to create a new approach that specifically addresses this goal. The approach is called a Bayesian spatial Dirichlet process clustered heterogeneous regression model. This non-parametric framework allows for inference on the number of clusters and the clustering configurations, while simultaneously estimating the parameters for each cluster. We demonstrate the efficacy of the proposed algorithm using simulated data and further apply it to analyse influential factors affecting children's health development domains in Queensland. The study provides valuable insights into the contributions of regional similarities in education and demographics to health outcomes, aiding targeted interventions and policy design.
Keywords:
GEOGRAPHICALLY WEIGHTED REGRESSION
BOUNDARY DETECTION
EXPANSION METHOD
MODELS
AUTOCORRELATION
SPLINES
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Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

Scientific Reports cover
Scientific Reports
IF:
3.9
Papers:
27.1W
Citations:
83.5W

Organization

No organization information available