Finite mixtures approach to ecological regression

Marco Alfò () and Cecilia Vitiello
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Marco Alfò: Università “La Sapienza” di Roma
Cecilia Vitiello: Università “La Sapienza” di Roma

Statistical Methods & Applications, 2003, vol. 12, issue 1, No 8, 93-108

Abstract: Abstract During past few years great attention has been devoted to the analysis of disease incidence and mortality rates, with an explicit focus on modelling geographical variation of rates observed in spatially adjacent regions. The general aim of these contributes has been both to highlight clusters of regions with homogeneous relative risk and to determine the effects of observed and unobserved risk factors related to the analyzed disease. Most of the proposed modelling approaches can be derived as alternative specifications of the components of a general convolution model (Molliè, 1996). In this paper, we consider the semiparametric approach discussed by Schlattmann and Böhning (1993); in particular, we focus on models with an explicit spatially structured component (see Biggeri et al., 2000), and propose alternative choices for the structure of the spatial component.

Keywords: Unknown risk factors; Nonparametric ML; Spatial association; Ecological regression (search for similar items in EconPapers)
Date: 2003
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DOI: 10.1007/BF02511586

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