Using latent variables in model based clustering: an e-government application. (English) Zbl 1407.62392
Oliveira, Paulo Eduardo (ed.) et al., Recent developments in modeling and applications in statistics. Selected papers based on the presentations at the 18th annual congress of the Portuguese Statistical Society, S. Pedro do Sul, Portugal, September 29 – October 2, 2010. Studies in Theoretical and Applied Statistics. Selected Papers of the Statistical Societies. Berlin: Springer. 3-11 (2013).
Summary: Besides continuous variables, binary indicators on ICT (information and communication technologies) infrastructures and utilities are usually collected in order to evaluate the quality of a public company and to define the policy priorities. In this chapter, we confront the problem of clustering public organizations with model-based clustering, and we assume each observed binary indicator to be generated from a latent continuous variable. The estimates of the scores of these variables allow us to use a fully Gaussian mixture model for classification.
For the entire collection see [Zbl 1254.62002].
For the entire collection see [Zbl 1254.62002].
MSC:
| 62P05 | Applications of statistics to actuarial sciences and financial mathematics |
| 60G15 | Gaussian processes |
| 62H30 | Classification and discrimination; cluster analysis (statistical aspects) |
Software:
Latent GOLDReferences:
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| [6] | Vermunt, J.K., Magidson, J.: Technical Guide for Latent GOLD 4.0: Basic and Advanced. Statistical Innovations Inc., Belmon, MA (2005) |
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