Open AccessBinomial-Beta Hierarchical Models for Ecological Inference
Author(s) -
Gary King,
Ori Rosen,
Martin A. Tanner
Publication year - 1999
Publication title -
sociological methods and research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.468
H-Index - 76
eISSN - 1552-8294
pISSN - 0049-1241
DOI - 10.1177/0049124199028001004
Subject(s) - covariate , markov chain monte carlo , inference , hierarchical database model , computer science , statistical inference , econometrics , negative binomial distribution , aggregate (composite) , data mining , statistics , ecology , mathematics , machine learning , artificial intelligence , bayesian probability , poisson distribution , biology , composite material , materials science
The authors develop binomial-beta hierarchical models for ecological inference using insights from the literature on hierarchical models based on Markov chain Monte Carlo algorithms and King's ecological inference model. The new approach reveals some features of the data that King's approach does not, can be easily generalized to more complicated problems such as general R × C tables, allows the data analyst to adjust for covariates, and provides a formal evaluation of the significance of the covariates. It may also be better suited to cases in which the observed aggregate cells are estimated from very few observations or have some forms of measurement error. This article also provides an examples of a hierarchical model in which the statistical idea of “borrowing strength” is used not merely to increase the efficiency of the estimates but to enable the data analyst to obtain estimates.
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