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- W60320555 abstract "When working with real-valued data regression analysis allows to model and forecast the values of a random variable in terms of the values of either another one or several other random variables defined on the same probability space. When data are not real-valued, regression techniques should be extended and adapted to model simply relationships in an effective way. Different kinds of imprecision may appear in experimental data: uncertainty in the quantification of the data, subjective measurements, perceptions, to name but a few. Compact intervals can be effectively used to represent these imprecise data. Set- and fuzzy-valued elements are also employed for representing different kinds of imprecise data. In this paper several linear regression estimation techniques for interval-valued data are revised. Both the practical applicability and the empirical behaviour of the estimation methods is studied by comparing the performance of the techniques under different population conditions." @default.
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- W60320555 date "2013-01-01" @default.
- W60320555 modified "2023-10-17" @default.
- W60320555 title "Linear Regression Analysis for Interval-valued Data Based on Set Arithmetic: A Review" @default.
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- W60320555 doi "https://doi.org/10.1007/978-3-642-30278-7_2" @default.
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