Linear regression model with histogram-valued variables
Linear regression model with histogram-valued variables
dc.contributor.author | Sónia Dias | en |
dc.contributor.author | Paula Brito | en |
dc.date.accessioned | 2017-12-20T22:28:34Z | |
dc.date.available | 2017-12-20T22:28:34Z | |
dc.date.issued | 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 | http://repositorio.inesctec.pt/handle/123456789/4586 | |
dc.identifier.uri | http://dx.doi.org/10.1002/sam.11260 | 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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