Robust inference for change points in high dimension

DOI10.1016/J.JMVA.2022.105114OpenAlexW4297982148MaRDI QIDQ2101465

Runmin Wang, Feiyu Jiang, Xiao-Feng Shao

Publication date: 6 December 2022

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2206.02738

zbMATH Keywords

change pointshigh-dimensional datasegmentationself-normalizationspatial sign

Mathematics Subject Classification ID

Nonparametric hypothesis testing (62G10) Hypothesis testing in multivariate analysis (62H15)

Related Items (1)

Consistency of \(p\)-norm based tests in high dimensions: characterization, monotonicity, domination

Uses Software

• MNM
• wbs

Cites Work

• Unnamed Item
• Change-point detection in panel data via double CUSUM statistic
• Wild binary segmentation for multiple change-point detection
• Uniform change point tests in high dimension
• Multivariate nonparametric methods with R. An approach based on spatial signs and ranks.
• A simple general approach to inference about the tail of a distribution
• On the efficiency of multivariate spatial sign and rank tests
• Empirical Bayesian analysis of simultaneous changepoints in multiple data sequences
• Tests for high-dimensional data based on means, spatial signs and spatial ranks
• Minimax rates in sparse, high-dimensional change point detection
• High dimensional change point inference: recent developments and extensions
• Inference for change points in high-dimensional data via selfnormalization
• A robust bootstrap change point test for high-dimensional location parameter
• A two-sample test for high-dimensional data with applications to gene-set testing
• Extensions of some classical methods in change point analysis
• Structural breaks in time series
• Change-point detection, segmentation, and related topics
• Linear Regression Limit Theory for Nonstationary Panel Data
• High Dimensional Change Point Estimation via Sparse Projection
• A Self-Normalized Approach to Confidence Interval Construction in Time Series
• A Unified Data-Adaptive Framework for High Dimensional Change Point Detection
• Finite Sample Change Point Inference and Identification for High-Dimensional Mean Vectors
• Two-Sample Test of High Dimensional Means Under Dependence
• Testing for Change Points in Time Series
• A High-Dimensional Nonparametric Multivariate Test for Mean Vector
• Self-Normalization for Time Series: A Review of Recent Developments
• Change‐point detection in panel data

This page was built for publication: Robust inference for change points in high dimension