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Fuzzy Optimization and Decision Making

Erschienen in:

11.09.2019

Least absolute deviations estimation for uncertain regression with imprecise observations

verfasst von: Zhe Liu, Ying Yang

Erschienen in: Fuzzy Optimization and Decision Making | Ausgabe 1/2020

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Abstract

Traditionally regression analysis answers questions about the relationships among variables based on the assumption that the observation values of variables are precise numbers. It has long been dominated by least squares, mostly due to the elegant theoretical foundation and ease of implementation. However, in many cases, we can only get imprecise observation values and the assumptions upon which the least squares is based may not be valid. So this paper characterizes the imprecise data in terms of uncertain variables and proposes a novel robust approach under the principle of least absolute deviations to estimate the unknown parameters in uncertain regression models. Furthermore, some general estimate approaches are also explored. Finally, numerical examples illustrate that our estimate is more robust than the least squares implying it is more suitable to handle observations with outliers.

Vorheriger Artikel Using local learning with fuzzy transform: application to short term forecasting problems

Nächster Artikel On product of positive L-R fuzzy numbers and its application to multi-period portfolio selection problems

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Titel: Least absolute deviations estimation for uncertain regression with imprecise observations
verfasst von: Zhe Liu
Ying Yang
Publikationsdatum: 11.09.2019
Verlag: Springer US
Erschienen in: Fuzzy Optimization and Decision Making / Ausgabe 1/2020
Print ISSN: 1568-4539
Elektronische ISSN: 1573-2908
DOI: https://doi.org/10.1007/s10700-019-09312-w

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