Using Latent Variables in Model Based Clustering: An E-Government Application

Chapter
Part of the Studies in Theoretical and Applied Statistics book series (STAS)

Abstract

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.

Keywords

Latent Variable Gaussian Mixture Model Quadrature Point Principal Component Score Local Dependency 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of EconomicsUniversity of Modena & Reggio EmiliaModenaItaly

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