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Spatial-Sign based Maxsum Test for High Dimensional Location Parameters
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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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Cited by 3 Pith papers
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Spatial-Sign based High dimensional Change Point Inference
The authors construct spatial-median and spatial-sign CUSUM statistics for high-dimensional changepoint testing, derive their null asymptotics and asymptotic independence, and combine them adaptively via Fisher's method.
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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.
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High-Dimensional Hettmansperger-Randles Estimator and its Applications
A banded high-dimensional Hettmansperger-Randles estimator is introduced and used to build robust location tests and a quadratic discriminant classifier for elliptical data.
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