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Linear regression with interval‐valued data

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Interval‐valued data refers to collection of observations in the form of intervals, rather than single numbers. It originally arose from situations of imprecision due to factors such as measurement or computation errors, where intervals are used to represent the true data points that are inside the intervals but not exactly known. Other circumstances include grouping and censoring. Recently, with the trend of big data, there is an increasing popularity of interval‐valued data resulting from data aggregation. In the past decades, a great deal of effort has been seen in the literature to investigate linear regression with interval‐value data. Various models that provide predictive tools and statistical inferences have been proposed and studied. The framework thus established is also well suited for both theoretical and computational advancements in the future. WIREs Comput Stat 2016, 8:54–60. doi: 10.1002/wics.1373 This article is categorized under: Statistical Models > Linear Models Algorithms and Computational Methods > Least Squares
A graphical illustration of the modeling ideas by Diamond (a) and Gil et al. (b). Graph (b) also illustrates model (4) or (5) by Gil et al., where b = BC and r = BR.
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Statistical Models > Linear Models
Algorithms and Computational Methods > Least Squares

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