Bayesian hierarchical models for misaligned data: a simulation study
Keywords:Bayesian analysis, Misaligned data, Linking spatial information
In this paper, the problem of combining information from different data sources is considered. We focus our attention on spatially misaligned data, where available information (typically counts or rates from administrative sources) refers to spatial units that are different from the ones of interest. A hierarchical Bayesian perspective is considered, as proposed by Mugglin et al. in 2000, to provide a fully model-based approach in an inferential, and not only descriptive, sense. In particular, explanatory covariates are arranged to be modeled according to spatial correlations through a conditionally autoregressive prior structure. In order to assess model performance and its robustness we generate artificial data inspired by a real study and a simulation exercise is then carried out.
S. BANERJEE, B. P. CARLIN, A. E. GELFAND (2004). Hierarchical Modeling and Analysis for Spatial Data. Chapman and Hall, London.
L. BERNARDINELLI, C. MONTOMOLI (1992). Empirical bayes versus fully Bayesian analysis of geographical variation in disease risk. Statistics in Medicine, 11.
J. BESAG (1974). Spatial interaction and the statistical analysis of lattice systems (with discussion). Journal of the Royal Statistical Society, series B, 36.
M. F. GOODCHILD, L. ANSELLIN, U. DEICHMANN (1993). A framework for the areal interpolation of socioeconomic data. Environment and Planning, A, no. 25, pp. 383–387.
C. A. GOTWAY, L. J. YOUNG (2002). Combining incompatible spatial data. Journal of the American Statistical Association, 97, no. 458, pp. 632–648.
A. GRYPARIS, C. J. PACIOREK, A. SCHWARTZ, B. COULL (2008). Measurement error caused by spatial misalignment in environmental epidemiology. Biostatistics, 10.
A. B. LAWSON (2009). Bayesian disease mapping. CRC press.
K. LOPIANO, L. YOUNG, C. GOTWAY (2014). A pseudo-penalized quasi-likelihood approach to the spatial misalignment problem with non-normal data. Biometrics, 70.
A. MUGGLIN, B. CARLIN (1998). Hierarchcal modeling in geographic information systems: Population interpolation over incompatible zones. Journal of Agricultural, Biological, and Environmental Statistics, 3.
A. MUGGLIN, B. CARLIN, A. GELFAND (2000). Fully model-based approaches for spatially misaligned data. Journal of the American Statistical Association, 95, no. 451, pp. 877–887.
A. MUGGLIN, B. CARLIN, L. ZHU, E. COLON (1999). Bayesian areal interpolation, estimation, and smoothing: An inferential approach for geographic information systems. Environment and Planning A, 3, no. 1, pp. 1337–1352.
R. PENG, L. BELL (2010). Spatial misalignment in time series studies of air pollution and health data. Biostatistics, 11, no. 4, p. 720740.
S. Sinclair, G. Pegram (2005). Combining radar and rain gauge rainfall estimates using conditional merging. Atmospheric Science Letters, 6, no. 1, pp. 19–22.
D. SPIEGELHALTER, A. THOMAS, N. BEST, D. LUNN (2003). WinBUGS User Manual, Version 1.4.
A. SZPIRO, L. SHEPPARD, T. LUMLEY (2011). Efficient measurement error correction with spatially misaligned data. Biostatistics, 12, no. 4, p. 610623.
A. VERDIN, B. RAJAGOPALAN, W. KLEIBER, C. FUNK (2015). A bayesian kriging approach for blending satellite and ground precipitation observations. Water Resources Research, 51.