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On the convenience of heteroscedasticity in highly multivariate disease mapping

Author

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  • F. Corpas-Burgos

    (Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunidad Valenciana (FISABIO)
    CIBER de Epidemiología y Salud Pública)

  • P. Botella-Rocamora

    (Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunidad Valenciana (FISABIO)
    Subdirección de Epidemiología, Vigilancia de la Salud y Sanidad Ambiental, Conselleria de Sanitat Universal i Salut Pública)

  • M. A. Martinez-Beneito

    (Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunidad Valenciana (FISABIO)
    CIBER de Epidemiología y Salud Pública)

Abstract

Highly multivariate disease mapping has recently been proposed as an enhancement of traditional multivariate studies, making it possible to perform the joint analysis of a large number of diseases. This line of research has an important potential since it integrates the information of many diseases into a single model yielding richer and more accurate risk maps. In this paper we show how some of the proposals already put forward in this area display some particular problems when applied to small regions of study. Specifically, the homoscedasticity of these proposals may produce evident misfits and distorted risk maps. In this paper we propose two new models to deal with the variance-adaptivity problem in multivariate disease mapping studies and give some theoretical insights on their interpretation.

Suggested Citation

  • F. Corpas-Burgos & P. Botella-Rocamora & M. A. Martinez-Beneito, 2019. "On the convenience of heteroscedasticity in highly multivariate disease mapping," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(4), pages 1229-1250, December.
    • Handle: RePEc:spr:testjl:v:28:y:2019:i:4:d:10.1007_s11749-019-00628-8
    DOI: 10.1007/s11749-019-00628-8
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    References listed on IDEAS

    as
    1. Miguel A. Martinez-Beneito, 2013. "A general modelling framework for multivariate disease mapping," Biometrika, Biometrika Trust, vol. 100(3), pages 539-553.
    2. Ying C. MacNab, 2018. "Some recent work on multivariate Gaussian Markov random fields," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(3), pages 497-541, September.
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    4. Ying C. MacNab, 2018. "Rejoinder on: Some recent work on multivariate Gaussian Markov random fields," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(3), pages 554-569, September.
    5. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    6. Birgit Schrödle & Leonhard Held, 2011. "A primer on disease mapping and ecological regression using $${\texttt{INLA}}$$," Computational Statistics, Springer, vol. 26(2), pages 241-258, June.
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    Cited by:

    1. Miguel A. Martinez-Beneito & Carlos Vergara-Hernández & Paloma Botella-Rocamora & Francisca Corpas-Burgos & Jordi Pérez-Panadés & Óscar Zurriaga & Elena Aldasoro & Carme Borrell & Elena Cabeza & Lluís, 2021. "Geographical Variability in Mortality in Urban Areas: A Joint Analysis of 16 Causes of Death," IJERPH, MDPI, vol. 18(11), pages 1-15, May.

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