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Probabilistic fuzzy systems (PFS) are shown to be valuable methods for conditional density estimation that combine fuzziness or linguistic uncertainty and probabilistic uncertainty. Several PFS applications have shown the added value of the different reasoning mechanisms of PFS and gains from incorporating two types of uncertainty. The effects of parametrization and parameter estimation on the function...
We consider conditional density approximation by fuzzy systems. Fuzzy systems are typically used to approximate deterministic functions in which the stochastic uncertainty is ignored. We propose probabilistic fuzzy systems (PFSs), in which the probabilistic nature of uncertainty is taken into account. These systems take also fuzzy uncertainty into account by their fuzzy partitioning of input and output...
Value at Risk (VaR) has been successfully estimated using single covariate probabilistic fuzzy systems (PFS), a method which combines a linguistic description of the system behaviour with statistical properties of data. In this paper, we consider VaR estimation based on a PFS model for density forecast of a continuous response variable conditional on a high-dimensional set of covariates. The PFS model...
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