Computing variance for interval data is NP-hard
ACM SIGACT News
Testing linear independence in linear models with interval-valued data
Computational Statistics & Data Analysis
Journal of Computational and Applied Mathematics - Special issue: Scientific computing, computer arithmetic, and validated numerics (SCAN 2004)
Centre and Range method for fitting a linear regression model to symbolic interval data
Computational Statistics & Data Analysis
Extended support vector interval regression networks for interval input-output data
Information Sciences: an International Journal
Estimation of a simple linear regression model for fuzzy random variables
Fuzzy Sets and Systems
Information Sciences: an International Journal
Constrained linear regression models for symbolic interval-valued variables
Computational Statistics & Data Analysis
A class of fuzzy clusterwise regression models
Information Sciences: an International Journal
Estimation of a flexible simple linear model for interval data based on set arithmetic
Computational Statistics & Data Analysis
Robust fuzzy regression analysis
Information Sciences: an International Journal
Fuzzy data treated as functional data: A one-way ANOVA test approach
Computational Statistics & Data Analysis
Information Sciences: an International Journal
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The prediction of a response random interval-valued set from an explanatory one has been examined in previous developments. These developments have considered an interval arithmetic-based linear model between the random interval-valued sets and a least squares regression analysis. The least squares approach involves a generalized L"2-metric between interval data; this metric weights squared distances between data location (mid-points/centers) and squared distances between data imprecision (spread/radius). As a consequence, estimators of the parameters in the linear model depend on the choice of the weights in the metric. To investigate about a suitable choice of weighting in the generalized mid/spread metric, a theoretical conclusion is first obtained. Finally, the impact of varying the weights is discussed by considering a Monte Carlo simulation study.