Sparse High-Dimensional Vector Autoregressive Bootstrap

2023-02-02 · Robert Adamek, Stephan Smeekes, Ines Wilms

We introduce a high-dimensional multiplier bootstrap for time series data based on capturing dependence through a sparsely estimated vector autoregressive model. We prove its consistency for inference on high-dimensional means under two different moment assumptions on the errors, namely sub-gaussian moments and a finite number of absolute moments. In establishing these results, we derive a Gaussian approximation for the maximum mean of a linear process, which may be of independent interest.

📄 PDF Abstract BibTeX arXiv:2302.01233

Code (0)

등록된 구현이 없습니다.

Tasks

Time SeriesTime Series Analysis Vocal Bursts Intensity Prediction

Structural Inference in Sparse High-Dimensional Vector Autoregressions

2020-07-30 · Jonas Krampe, Efstathios Paparoditis, Carsten Trenkler

We consider statistical inference for impulse responses in sparse, structural high-dimensional vector autoregressive (SVAR) systems. We introduce consistent estimators of impulse responses in the high-dimensional setting…

Time Series Analysis valid Vocal Bursts Intensity Prediction

Sparse Principal Component Analysis for High Dimensional Vector Autoregressive Models

2013-06-30 · Zhaoran Wang, Fang Han, Han Liu

We study sparse principal component analysis for high dimensional vector autoregressive time series under a doubly asymptotic framework, which allows the dimension $d$ to scale with the series length $T$. We treat the tr…

Time Series Time Series Analysis Vocal Bursts Intensity Prediction

On Statistical Inference for High-Dimensional Binary Time Series

2025-11-29 · Dehao Dai, Yunyi Zhang arxiv

The analysis of non-real-valued data, such as binary time series, has attracted great interest in recent years. This manuscript proposes a post-selection estimator for estimating the coefficient matrices of a high-dimens…

Missing Data in Sparse Transition Matrix Estimation for Sub-Gaussian Vector Autoregressive Processes

2018-02-26 · Amin Jalali, Rebecca Willett

High-dimensional time series data exist in numerous areas such as finance, genomics, healthcare, and neuroscience. An unavoidable aspect of all such datasets is missing data, and dealing with this issue has been an impor…

Time Series Time Series Analysis

Sparse Tucker Decomposition and Graph Regularization for High-Dimensional Time Series Forecasting

2026-01-01 · Sijia Xia, Michael K. Ng, Xiongjun Zhang arxiv

Existing methods of vector autoregressive model for multivariate time series analysis make use of low-rank matrix approximation or Tucker decomposition to reduce the dimension of the over-parameterization issue. In this …

Time Series Forecasting Time Series Analysis