In this paper we present a first attempt to define a statistically coherent protocol for Environmental Risk Assessment (ERA), considered as a classification problem. Our approach moves from an idea developed in the pattern recognition literature. Several independent classifiers, each working on a subset of the covariates space, produce a set of corresponding units classifications. Then a gate, modulating the partial results, produces a final, combined classification. We propose a combined classification strategy based on rank transformations and Bayesian mixture classifiers. Although the use of Bayesian mixture models in classification problems is quite common in many fields of application, the novelty of our proposal concerns the use of truncated Gaussian components to model the behaviour of the rank variables in a multidimensional setting. We approach the general problem by partitioning the covariate space into several subspaces, each one representing one environmental dimension; we consider three environmental dimensions (Air, Water and Waste), represented by several pressure indicators. The evaluation of environmental risk for the Tuscany municipalities is our motivating example.

Environmental Risk Assessment in the Tuscany Region: a Proposal

DIVINO, Fabio;
2007

Abstract

In this paper we present a first attempt to define a statistically coherent protocol for Environmental Risk Assessment (ERA), considered as a classification problem. Our approach moves from an idea developed in the pattern recognition literature. Several independent classifiers, each working on a subset of the covariates space, produce a set of corresponding units classifications. Then a gate, modulating the partial results, produces a final, combined classification. We propose a combined classification strategy based on rank transformations and Bayesian mixture classifiers. Although the use of Bayesian mixture models in classification problems is quite common in many fields of application, the novelty of our proposal concerns the use of truncated Gaussian components to model the behaviour of the rank variables in a multidimensional setting. We approach the general problem by partitioning the covariate space into several subspaces, each one representing one environmental dimension; we consider three environmental dimensions (Air, Water and Waste), represented by several pressure indicators. The evaluation of environmental risk for the Tuscany municipalities is our motivating example.
http://onlinelibrary.wiley.com/doi/10.1002/env.831/abstract
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11695/3571
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