Abstract
In this section we show how to model clustered seasonal data. We look at two types of clustered data: longitudinal data and spatial data.
Longitudinal data are time series data from multiple subjects or clusters (e.g., cities). They may be equally spaced (e.g., every week) or irregularly spaced. If each subject has the same number of responses then the data are balanced, otherwise they are unbalanced.
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References
Barnett, A.G., Fraser, J.F., de Looper, M.: The seasonality in heart failure deaths and total cardiovascular deaths in Australia. Aust. N. Z. J. Public Health 32(5), 408–413 (2008)
Diggle, P., Ribeiro, P.J.: Model-Based Geostatistics. Springer Series in Statistics. Springer, New York (2007)
Elliot, P., Wakefield, J., Best, N., Briggs, D.: Spatial Epidemiology: Methods and Applications. Oxford University Press, Oxford (2000)
The Eurowinter Group: Cold exposure and winter mortality from ischaemic heart disease, cerebrovascular disease, respiratory disease, and all causes in warm and cold regions of Europe. Lancet 349(9062), 1341–1346 (1997)
Venables, W.N., Ripley, B.D.: Modern Applied Statistics with S, 4th edn. Springer, New York (2002)
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Barnett, A.G., Dobson, A.J. (2010). Clustered Seasonal Data. In: Analysing Seasonal Health Data. Statistics for Biology and Health. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10748-1_6
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DOI: https://doi.org/10.1007/978-3-642-10748-1_6
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