title:         An Incomplete Data Approach to the Ecological Inference Problem
authors:       Kosuke Imai, Ying Lu
entrydate:     2005-09-12 09:00:30
keywords:      Coarse data, Contextual effects,
Data augmentation, EM algorithm, Missing information principle,
Nonparametric Bayesian Modeling.
abstract:      In this paper, we propose to formulate ecological inference as a
coarse data problem where only a subset of the complete-data sample
space is observed.  Applying the related assumptions and theoretical
results of Heitjan and Rubin (1991), we formally identify three key
factors that affect ecological inference; distributional, contextual
and aggregation effects. Different modeling strategies are discussed to
deal with distributional and contextual effects. While aggregation
effects cannot be statistically adjusted, we show how to formally
quantify the magnitude of such effects through the use of the
Expectation-Maximization algorithm. The paper concludes with
simulations and empirical applications that assess the performance of
the proposed models. C-code used to implement the proposed method is
available with easy-to-use R interface.


http://polmeth.wustl.edu/retrieve.php?id=560

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