Dipartimento di Scienze Statistiche

PhD program in Statistics DSS    Statistics Seminar October 22, 2024, 12:00    In person.Room 34 (CU002)    Webinar^.https://uniroma1.zoom.us/j/86881977368?pwd=SWRFc VFjMDZTa0lXZk05TE1zNm5adz09 Passcode: 432940    Online Multivariate Changepoint Detection: Leveraging Links With Computational Geometry      Guillem Rigaill INRAE, LaMME (Laboratoire de Mathématiques et Modélisation d'Évry)    The increasing volume of data streams poses significant computational challenges for detecting changepoints online. Likelihood-based methods are effective, but a naive sequential implementation becomes impractical online due to high computational costs. We develop an online algorithm that exactly calculates the likelihood ratio test for a single changepoint in pdimensional data streams by leveraging fascinating connections with computational geometry. This connection straightforwardly allows us to recover sparse likelihood ratio statistics exactly: that is assuming only a subset of the dimensions are changing. Our algorithm is straightforward, fast, and apparently quasi-linear. A dyadic variant of our algorithm is provably quasi-linear, being Op(nlog(n) p+1) for n data points and p less than 3, but slower in practice. These algorithms are computationally impractical when p is larger than 5, and we provide an approximate algorithm suitable for such p which is Op(np̃log(n) p̃+1), for some user-specified p̃≤ 5. We derive some statistical guarantees for the proposed procedures in the Gaussian case, and confirm the good computational and statistical performance, and usefulness, of the algorithms on both empirical data and on NBA data.    Joint work with Liudmila Pishchagina, Gaetano Romano, Paul Fearnhead and Vincent Runge. Link-arxiv : https://arxiv.org/abs/2311.01174     In allegato la locandina con l'abstract e i riferimenti per partecipare al seminario in presenza e online.