Linear regression model with histogram-valued variables Sónia Dias en Paula Brito en 2017-12-20T22:28:34Z 2017-12-20T22:28:34Z 2015 en
dc.description.abstract Histogram-valued variables are a particular kind of variables studied in Symbolic Data Analysis where to each entity under analysis corresponds a distribution that may be represented by a histogram or by a quantile function. Linear regression models for this type of data are necessarily more complex than a simple generalization of the classical model: the parameters cannot be negative; still the linear relation between the variables must be allowed to be either direct or inverse. In this work, we propose a new linear regression model for histogram-valued variables that solves this problem, named Distribution and Symmetric Distribution Regression Model. To determine the parameters of this model, it is necessary to solve a quadratic optimization problem, subject to non-negativity constraints on the unknowns; the error measure between the predicted and observed distributions uses the Mallows distance. As in classical analysis, the model is associated with a goodness-of-fit measure whose values range between 0 and 1. Using the proposed model, applications with real and simulated data are presented. © 2015 Wiley Periodicals, Inc. en
dc.identifier.uri en
dc.language eng en
dc.relation 4984 en
dc.relation 5739 en
dc.rights info:eu-repo/semantics/openAccess en
dc.title Linear regression model with histogram-valued variables en
dc.type article en
dc.type Publication en
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