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Brigitte Le Roux studies geometric data analysis, including correspondence analysis and principal component analysis. Her research account describes investigations of how principal axes change when observations, weights or the underlying Euclidean geometry are modified. This stability question asks which apparent structures survive a change in the analysis, rather than treating every fitted axis as intrinsic to the data.^[ref]

Her joint book with Henry Rouanet, Geometric Data Analysis by Brigitte Le Roux and Henry Rouanet, develops the weighted-cloud approach through correspondence analysis, Euclidean clouds and principal component analysis. It connects the linear algebra of projections with explicit choices about representation of multivariate observations.^[ref]

Henry Rouanet worked on statistics for researchers in psychology and the social sciences, emphasizing interpretable methods for analyzing actual data. His own account describes that aim and organizes his work by statistical problem rather than by an abstract sequence of prerequisites.^[ref]

With Brigitte Le Roux, he wrote Geometric Data Analysis by Brigitte Le Roux and Henry Rouanet. Its treatment of correspondence analysis and principal component analysis expresses multivariate data as weighted clouds in Euclidean geometry. The choice of weights and metric specifies which departures count as large; the resulting picture is part of the statistical model, not simply an illustration added afterward.^[ref]