Lasso Estimation of an Interval-Valued Multiple Regression Model
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Abstract
A multiple interval-valued linear regression model considering all the cross-relationships between the mids and spreads of the intervals has been introduced recently. A least-squares estimation of the regression parameters has been carried out by transforming a quadratic optimization problem with inequality constraints into a linear complementary problem and using Lemke's algorithm to solve it. Due to the irrelevance of certain cross-relationships, an alternative estimation process, the LASSO (Least Absolut Shrinkage and Selection Operator), is developed. A comparative study showing the differences between the proposed estimators is provided.Keywords
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This publication has 6 references indexed in Scilit:
- Estimation of a flexible simple linear model for interval data based on set arithmeticComputational Statistics & Data Analysis, 2011
- Constrained linear regression models for symbolic interval-valued variablesComputational Statistics & Data Analysis, 2010
- A new family of metrics for compact, convex (fuzzy) sets based on a generalized concept of mid and spreadInformation Sciences, 2009
- Least squares estimation of linear regression models for convex compact random setsAdvances in Data Analysis and Classification, 2007
- Adapting Particle Filter on Interval Data for Dynamic State Estimation2007
- Linear Statistical Inference for Random Fuzzy DataStatistics, 1997