10 papers · 1 filter
Sparse -spatial-median clustering for high-dimensional data
Ping Zhao, Dan Zhuang, Long Feng
We propose a robust clustering framework for high-dimensional data with heavy tails and a large fraction of irrelevant variables. The method replaces the mean updates of Lloyd's $K…
Robust Spatial-Sign-Based Testing of High-Dimensional Alpha in Conditional Factor Models
Ping Zhao, Hongfei Wang
This paper develops a new framework for alpha testing in high-dimensional factor pricing models with time-varying coefficients. To detect sparse alternatives, we propose a spatial-…
High dimensional alpha test for linear factor pricing model with -norm
Ping Zhao, Huifang Ma, Long Feng
We consider testing zero pricing errors in high-dimensional linear factor pricing models. Existing methods are mainly based on either an statistic, which is effective under d…
Rank-based Maxsum test for high dimensional regression coefficient
Ping Zhao, Liangliang Yuan
We study global inference for regression coefficients in high-dimensional linear models under potentially heavy-tailed errors. While sum-type tests are powerful for dense alternati…
Note on High Dimensional Spatial-Sign Test for One Sample Problem
Ping Zhao, Long Feng
We revisit the null distribution of the high-dimensional spatial-sign test of Wang et al. (2015) under mild structural assumptions on the scatter matrix. We show that the standardi…
Inverse Norm Weighted Maxsum Test for High Dimensional Location Parameters
Guowei Yan, Ping Zhao, Long Feng
In the context of high-dimensional data, we investigate the one-sample location testing problem. We introduce a max-type test based on the weighted spatial sign, which exhibits exc…