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|Title:||On the estimation of the regression model M for interval data||Authors:||Garciá-Bárzana, Marta
Kontoghiorghes, Erricos John
|Keywords:||Interval data;Regression model||Category:||Economics and Business||Field:||Social Sciences||Issue Date:||1-Dec-2013||Publisher:||Springer Science+Business Media||Source:||Towards advanced data analysis by combining soft computing and statistics, 2013, Pages 43-52||metadata.dc.doi:||10.1007/978-3-642-30278-7_4||Abstract:||A linear regression model for interval data based on the natural interval-arithmetic has recently been proposed. Interval data can be identified with 2-dimensional points in ℝ x ℝ+, since they can be parametrized by its mid-point and its semi-amplitude or spread, which is non-negative. The model accounts separately for the contribution of the mid-points and the spreads through a single equation. The least squares estimation becomes a quadratic optimization problem subject to linear constraints, which guarantee the existence of the residuals. Several estimators are discussed. Namely, a closed-form estimator, the restricted least-squares estimator, an empirical estimator and an estimator based on separate models for mids and spreads have been investigated. Real-life examples are considered. Simulations are performed in order to assess the consistency and the bias of the estimators. Results indicate that the numerical and the closed-form estimator are appropriate in most of cases, while the empirical estimator and the one based on separate models are not always suitable.||URI:||http://ktisis.cut.ac.cy/handle/10488/9757||ISSN:||14349922||Rights:||© Springer-Verlag 2013.||Type:||Book Chapter|
|Appears in Collections:||Κεφάλαια βιβλίων/Book chapters|
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