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Bivariate symbolic regression models for interval-valued variables. (English) Zbl 1431.62328

Summary: Interval-valued variables have become very common in data analysis. Up until now, symbolic regression mostly approaches this type of data from an optimization point of view, considering neither the probabilistic aspects of the models nor the nonlinear relationships between the interval response and the interval predictors. In this article, we formulate interval-valued variables as bivariate random vectors and introduce the bivariate symbolic regression model based on the generalized linear models theory which provides much-needed exibility in practice. Important inferential aspects are investigated. Applications to synthetic and real data illustrate the usefulness of the proposed approach.

MSC:

62J86 Fuzziness, and linear inference and regression
62J12 Generalized linear models (logistic models)

Software:

SODAS
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Full Text: DOI

References:

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