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This paper considers the problem of simultaneously testing a large number of general linear hypotheses, encompassing covariate-effect analysis, analysis of variance, and model comparisons. For large-scale multivariate regression, we develop a set of robust inference methods to explore data features, such as heavy tailedness and skewness, which are invisible to the scope of least squares.

How underlying mechanisms bias evolution toward predictable outcomes remains an area of active debate. In this study, we leveraged …