An inverse-norm weighted spatial-sign max-sum test is introduced and shown, asymptotically and in simulations, to be powerful across sparse and dense alternatives for heavy-tailed high-dimensional data.
Spatial-Sign based Maxsum Test for High Dimensional Location Parameters
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abstract
In this study, we explore a robust testing procedure for the high-dimensional location parameters testing problem. Initially, we introduce a spatial-sign based max-type test statistic, which exhibits excellent performance for sparse alternatives. Subsequently, we demonstrate the asymptotic independence between this max-type test statistic and the spatial-sign based sum-type test statistic (Feng and Sun, 2016). Building on this, we propose a spatial-sign based max-sum type testing procedure, which shows remarkable performance under varying signal sparsity. Our simulation studies underscore the superior performance of the procedures we propose.
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Inverse Norm Weighted Maxsum Test for High Dimensional Location Parameters
An inverse-norm weighted spatial-sign max-sum test is introduced and shown, asymptotically and in simulations, to be powerful across sparse and dense alternatives for heavy-tailed high-dimensional data.