{"ID":3049934,"CreatedAt":"2026-06-04T02:13:16.786527022Z","UpdatedAt":"2026-06-06T15:44:26.945507316Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.05072","arxiv_id":"2606.05072","title":"Adaptive Sequential Change Detection using Mixtures of Predictive Distributions","abstract":"This paper studies the problem of detecting a change in the distribution of a sequence of independent observations when the post-change distribution is unknown. We propose a novel change detection algorithm, termed Predictive-Mixture CuSum (PM-CuSum), which combines predictive distributions constructed from sliding windows of different lengths within a CuSum recursion. The predictive distributions are aggregated using adaptive weights based on their recent predictive performance. We show that PM-CuSum achieves first-order asymptotic optimality under mild conditions, and that its asymptotic delay bound has a smaller remainder order than what is achieved by any fixed (even oracle) window. Numerical simulations demonstrate that PM-CuSum performs well compared to existing methods. Moreover, it is demonstrated that forming likelihood ratios using full predictive distributions can substantially improve performance compared to plug-in likelihoods.","short_abstract":"This paper studies the problem of detecting a change in the distribution of a sequence of independent observations when the post-change distribution is unknown. We propose a novel change detection algorithm, termed Predictive-Mixture CuSum (PM-CuSum), which combines predictive distributions constructed from sliding win...","url_abs":"https://arxiv.org/abs/2606.05072","url_pdf":"https://arxiv.org/pdf/2606.05072v1","authors":"[\"Topi Halme\",\"H. Vincent Poor\",\"Visa Koivunen\"]","published":"2026-06-03T16:30:47Z","proceeding":"math.ST","tasks":"[\"math.ST\"]","methods":"[]","has_code":false}