On Statistical Inference for High-Dimensional Binary Time Series
Fuente:
arXiv
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914177032912896 |
|---|---|
| author | Dai, Dehao Zhang, Yunyi |
| author_facet | Dai, Dehao Zhang, Yunyi |
| contents | 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-dimensional generalized binary vector autoregressive process and establishes a Gaussian approximation theorem for the proposed estimator. Furthermore, it introduces a second-order wild bootstrap algorithm to enable statistical inference on the coefficient matrices. Numerical studies and empirical applications demonstrate the good finite-sample performance of the proposed method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_00338 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | On Statistical Inference for High-Dimensional Binary Time Series Dai, Dehao Zhang, Yunyi Methodology Statistics Theory Applications Machine Learning 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-dimensional generalized binary vector autoregressive process and establishes a Gaussian approximation theorem for the proposed estimator. Furthermore, it introduces a second-order wild bootstrap algorithm to enable statistical inference on the coefficient matrices. Numerical studies and empirical applications demonstrate the good finite-sample performance of the proposed method. |
| title | On Statistical Inference for High-Dimensional Binary Time Series |
| topic | Methodology Statistics Theory Applications Machine Learning |
| url | https://arxiv.org/abs/2512.00338 |