{"ID":2835104,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.00338","arxiv_id":"2512.00338","title":"On Statistical Inference for High-Dimensional Binary Time Series","abstract":"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.","short_abstract":"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 theo...","url_abs":"https://arxiv.org/abs/2512.00338","url_pdf":"https://arxiv.org/pdf/2512.00338v2","authors":"[\"Dehao Dai\",\"Yunyi Zhang\"]","published":"2025-11-29T06:03:32Z","proceeding":"stat.ME","tasks":"[\"stat.ME\",\"math.ST\",\"stat.AP\",\"stat.ML\"]","methods":"[]","has_code":false}