Ecological Bias: Use of Maximum‐Entropy Approximations

Noel Cressie, Sylvia Richardson and Isabelle Jaussent

Australian & New Zealand Journal of Statistics, 2004, vol. 46, issue 2, 233-255

Abstract: The focus of geographical studies in epidemiology has recently moved towards looking for effects of exposures based on data taken at local levels of aggregation (i.e. small areas). This paper investigates how regression coefficients measuring covariate effects at the point level are modified under aggregation. Changing the level of aggregation can lead to completely different conclusions about exposure–effect relationships, a phenomenon often referred to as ecological bias. With partial knowledge of the within‐area distribution of the exposure variable, the notion of maximum entropy can be used to approximate that part of the distribution that is unknown. From the approximation, an expression for the ecological bias is obtained; simulations and an example show that the maximum‐entropy approximation is often better than other commonly used approximations.

Date: 2004
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1111/j.1467-842X.2004.00327.x

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:anzsta:v:46:y:2004:i:2:p:233-255

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=1369-1473

Access Statistics for this article

Australian & New Zealand Journal of Statistics is currently edited by Chris J. Lloyd, Rob J. Hyndman and Russell B. Millar

More articles in Australian & New Zealand Journal of Statistics from Australian Statistical Publishing Association Inc.
Bibliographic data for series maintained by Wiley Content Delivery ().