Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/9757
Title: On the estimation of the regression model M for interval data
Authors: Garciá-Bárzana, Marta 
Colubi, Ana 
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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