Centre and Range method for fitting a linear regression model to symbolic interval data
Computational Statistics & Data AnalysisPublished 30 April 2007
Eufrásio de Andrade Lima Neto, Francisco de A.T. de Carvalho
Citations267
SJR quartileQ1
SJR score0.89
SNIP1.38
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TL;DR
A new approach to fitting a linear regression model to symbolic interval data based on the estimation of the average behaviour of both the root mean square error and the square of the correlation coefficient in the framework of a Monte Carlo experiment is introduced.
Abstract
This paper introduces a new approach to fitting a linear regression model to symbolic interval data. Each example of the learning set is described by a feature vector, for which each feature value is an interval. The new method fits a linear regression model on the mid-points and ranges of the interval values assumed by the variables in the learning set. The prediction of the lower and upper bounds of the interval value of the dependent variable is accomplished from its mid-point and range, which are estimated from the fitted linear regression model applied to the mid-point and range of each interval value of the independent variables. The assessment of the proposed prediction method is based on the estimation of the average behaviour of both the root mean square error and the square of the correlation coefficient in the framework of a Monte Carlo experiment. Finally, the approaches presented in this paper are applied to a real data set and their performance is compared.
Keywords
ChemistryComputer ScienceMathematics
TechnometricsApplied Regression Analysis
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