6 papers
High-Dimensional Change Point Analysis for Temporally Dependent Data
Xiaoyi Wang, Le Zhou, Jixuan Liu +1
This paper develops adaptive procedures for detecting and locating mean changes in high-dimensional time series. Quadratic CUSUM statistics target dense changes, whereas coordinate…
Adaptive Change Point Inference for High Dimensional Time Series with Temporal Dependence
Xiaoyi Wang, Jixuan Liu, Long Feng
This paper investigates change point inference in high-dimensional time series. We begin by introducing a max--norm based test procedure, which demonstrates strong performance…
Tensor Elliptical Graphic Model
Jixuan Liu, Zhengke Lu, Le Zhou +2
We address the problem of robust estimation of sparse high dimensional tensor elliptical graphical model. Most of the research focus on tensor graphical model under normality. To e…
Spatial-Sign based High dimensional Change Point Inference
Jixuan Liu, Long Feng, Liuhua Peng +1
High-dimensional changepoint inference, adaptable to diverse alternative scenarios, has attracted significant attention in recent years. In this paper, we propose an adaptive and r…
TUNE: Algorithm-Agnostic Inference after Changepoint Detection
Yinxu Jia, Jixuan Liu, Guanghui Wang +2
In multiple changepoint analysis, assessing the uncertainty of detected changepoints is crucial for enhancing detection reliability -- a topic that has garnered significant attenti…
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…