17 papers
Cauchy-Aggregated Ridge Tests for High-Dimensional Factor Pricing Models
Ping Zhao, Dan Zhuang
In high-dimensional factor pricing models, the power of regularized alpha tests depends on the ridge parameter, whose optimal value varies with the unknown direction of pricing err…
Cauchy-Combined Hettmansperger-Randles Location Tests in High Dimensions
Ping Zhao, Long Feng, Xiaoyi Wang
High-dimensional mean testing is challenging under strong dependence and heavy tails, since classical Hotelling statistics are ill posed and regularized versions based on sample mo…
Adaptive Ridge-Regularized Hotelling Change-Point Tests for Functional Data
Ping Zhao, Long Feng
We propose a unified ridge-regularized Hotelling framework for detecting and locating mean changes in functional time series. A growing basis expansion converts the functional obse…
Cauchy Aggregation of Ridge-Regularized Hotelling Tests for High-Dimensional Change-Point Detection
Ping Zhao, Le Zhou, Long Feng
Ridge-regularized Hotelling-type (RHT) change-point tests depend on a ridge parameter , but the power-optimal value is determined by the unknown covariance structure and the unk…
Rank-Based Tests for Mutual Independence of High-Dimensional Random Vectors via Norm
Ping Zhao, Hongfei Wang, Long Feng
We consider the problem of testing mutual independence among the components of a high-dimensional random vector. Building on the rank-based max-sum framework, we introduce fixed fi…
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…
Robust Mutual Fund Selection with False Discovery Rate Control
Hongfei Wang, Long Feng, Ping Zhao +1
In this article, we address the challenge of identifying skilled mutual funds among a large pool of candidates, utilizing the linear factor pricing model. Assuming observable facto…
Adaptive Sphericity Tests for High Dimensional Data
Ping Zhao, Wenwan Yang, Long Feng +1
In this paper, we investigate sphericity testing in high-dimensional settings, where existing methods primarily rely on sum-type test procedures that often underperform under spars…
Double Robust high dimensional alpha test for linear factor pricing model
Ping Zhao, Long Feng, Hongfei Wang +1
In this paper, we investigate alpha testing for high-dimensional linear factor pricing models. We propose a spatial sign-based max-type test to handle sparse alternative cases. Add…
Spatial Sign based Principal Component Analysis for High Dimensional Data
Ping Zhao, Hongfei Wang, Long Feng
This article focuses on the robust principal component analysis (PCA) of high-dimensional data with elliptical distributions. We investigate the PCA of the sample spatial-sign cova…
Adaptive Strategy of Testing Alphas in High Dimensional Linear Factor Pricing Models
Chenxi Zhao, Ping Zhao, Long Feng +1
In recent years, there has been considerable research on testing alphas in high-dimensional linear factor pricing models. In our study, we introduce a novel max-type test procedure…
Spatial-Sign based Maxsum Test for High Dimensional Location Parameters
Jixuan Liu, Long Feng, Ping Zhao +1
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 statis…