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Estimation With Heteroscedastic And Correlated Errors: A Spatial Analysis Of Intra‐Urban Mortality Data

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  • Robert Haining

Abstract

ABSTRACT Regression analyses using data collected across contiguous a real units of varying size often fail to satisfy the statistical assumptions of ordinary least squares estimation, since errors arc neither homoscedaslic nor uncorrelated. This paper considers regression analysis in the case of mortality data collected across 87 contiguous Community Medicine Areas defined for the Creater Glasgow conurbation. Data properties are identified using exploratory techniques. A statistical model is specified, parameters estimated udner alternative assumptions about the errors, and the fit assessed using both confirmatory and exploratory methods. A pricipal objective of the study is to identify areas that have particularly high or low mortality rates.

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  • Robert Haining, 1991. "Estimation With Heteroscedastic And Correlated Errors: A Spatial Analysis Of Intra‐Urban Mortality Data," Papers in Regional Science, Wiley Blackwell, vol. 70(3), pages 223-241, July.
    • Handle: RePEc:bla:presci:v:70:y:1991:i:3:p:223-241
    DOI: 10.1111/j.1435-5597.1991.tb01730.x
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    Cited by:

    1. Peter Congdon, 2000. "Monitoring Suicide Mortality: A Bayesian Approach," European Journal of Population, Springer;European Association for Population Studies, vol. 16(3), pages 251-284, September.
    2. Peter Congdon, 1997. "Multilevel and Clustering Analysis of Health Outcomes in Small Areas," European Journal of Population, Springer;European Association for Population Studies, vol. 13(4), pages 305-338, December.
    3. Peter Congdon & Alan Smith & Christine Dean, 1998. "Assessing Psychiatric Morbidity from Community Registers: Methods for Bayesian Adjustment," Urban Studies, Urban Studies Journal Limited, vol. 35(12), pages 2323-2352, December.

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