From the Statistics of Data to the Statistics of Knowledge

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Centre and Range method for fitting a linear regression model to symbolic interval data (2008, methodology): “Regarding univariate statistics, Bertrand and Goupil (2000) and Billard and Diday (2003) introduced central tendency and dispersion measures suitable for symbolic interval data.”
Modelling interval data with Normal and Skew-Normal distributions (2012, background): “Symbolic data analysis [5,7,11] provides a framework where new variable types allow to take directly into account variability and/or uncertainty associated to each single “individual”, avoiding restrictive summarizations to impose a fit to the classical representation structure.”
Constrained Regression for Interval-Valued Data (2013, neutral): “Within the symbolic approach (Billard and Diday 2003, 2006), there are several proposals to fit a regression model to inter- val data.”
Symbolic Data Analysis: Conceptual Statistics and Data Mining (Wiley Series in Computational Statistics) (2007, background): “…of patients served through a particular medical insurance company, say) was aggregated in such a way that it produced the data of Table 3.2 (from Billard and Diday, 2003, Table 3) representing the outcomes relative to the presence of Cancer Y1 with 1 = No = 0 Yes = 1 and Number Y2 of…”