A Type of Bayesian Small Area Estimation for the Analysis of Cancer Mortality Data

Schach, U.

doi:10.1007/978-3-642-55721-7_37

U. Schach⁶

Part of the book series: Studies in Classification, Data Analysis, and Knowledge Organization ((STUDIES CLASS))

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Abstract

The distribution of cancer mortality in Germany is collected in two different data sets, one with a high spatial resolution but aggregated data over time, the other with yearly data on a coarse spatial scale. This is due to privacy protection laws, as the data become nearly individual when analyzing rare cancer types or strata of age groups. The aim of this paper is to present a modeling approach to estimate the missing data from the given spatial and temporal marginals. Parameters of spatial and temporal autocorrelation, dispersion, and temporal trend parameters are estimated simultaneously within the Bayesian model, using MCMC techniques based on the Metropolis-Hastings algorithm.

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Fachbereich Statistik, Universität Dortmund, D-44221, Dortmund, Germany
U. Schach

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U. Schach
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Munich School of Management Institute of Corporate Development and Organization, University of Munich, Kaulbachstraße 45/1, 80539, Munich, Germany
Manfred Schwaiger
Department of Mathematical Methods in Economics, University of Augsburg, Universitätsstraße 16, 86159, Augsburg, Germany
Otto Opitz

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Schach, U. (2003). A Type of Bayesian Small Area Estimation for the Analysis of Cancer Mortality Data. In: Schwaiger, M., Opitz, O. (eds) Exploratory Data Analysis in Empirical Research. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-55721-7_37

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DOI: https://doi.org/10.1007/978-3-642-55721-7_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44183-0
Online ISBN: 978-3-642-55721-7
eBook Packages: Springer Book Archive

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