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Hierarchical modeling in spatial epidemiology
Author(s) -
Lawson Andrew B.
Publication year - 2014
Publication title -
wiley interdisciplinary reviews: computational statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.693
H-Index - 38
eISSN - 1939-0068
pISSN - 1939-5108
DOI - 10.1002/wics.1315
Subject(s) - computer science , data science , data mining , computational statistics , bayesian probability , health informatics , informatics , software , spatial analysis , hierarchical database model , spatial epidemiology , epidemiology , statistics , machine learning , artificial intelligence , mathematics , medicine , public health , nursing , electrical engineering , programming language , engineering
This paper considers the basic concepts and methods used in hierarchical modeling for data arising in spatial epidemiology. Following discussion of basic statistical and epidemiological concepts relevant to small‐area health studies, the paper reviews the different approaches to model formulation, parameter estimation, and also software resources. WIREs Comput Stat 2014, 6:405–417. doi: 10.1002/wics.1315 This article is categorized under: Statistical and Graphical Methods of Data Analysis > Bayesian Methods and Theory Applications of Computational Statistics > Health and Medical Data/Informatics Data: Types and Structure > Image and Spatial Data