{"660486":{"#nid":"660486","#data":{"type":"event","title":"ISyE Statistical Seminar- Yu Yi","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003EBio\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EI am a Reader in the Department of Statistics, University of Warwick and a Turing Fellow at the Alan Turing Institute, previously an Associate Professor in the University of Warwick, a Lecturer in the University of Bristol, a postdoc of Professor Richard Samworth and a graduate student of Professor Zhiliang Ying. I obtained my academic degrees from Fudan University (B.Sc. in Mathematics, June 2009 and Ph.D. in Mathematical Statistics, June 2013).\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EAbstract\u0026nbsp;\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EThis paper concerns about the limiting distributions of change point\u003Cbr \/\u003E\r\nestimators, in a high-dimensional linear regression time series context, where\u003Cbr \/\u003E\r\na regression object $(y_t, X_t) \\in \\mathbb{R} \\times \\mathbb{R}^p$ is observed\u003Cbr \/\u003E\r\nat every time point $t \\in \\{1, \\ldots, n\\}$. At unknown time points, called\u003Cbr \/\u003E\r\nchange points, the regression coefficients change, with the jump sizes measured\u003Cbr \/\u003E\r\nin $\\ell_2$-norm. We provide limiting distributions of the change point\u003Cbr \/\u003E\r\nestimators in the regimes where the minimal jump size vanishes and where it\u003Cbr \/\u003E\r\nremains a constant. We allow for both the covariate and noise sequences to be\u003Cbr \/\u003E\r\ntemporally dependent, in the functional dependence framework, which is the\u003Cbr \/\u003E\r\nfirst time seen in the change point inference literature. We show that a\u003Cbr \/\u003E\r\nblock-type long-run variance estimator is consistent under the functional\u003Cbr \/\u003E\r\ndependence, which facilitates the practical implementation of our derived\u003Cbr \/\u003E\r\nlimiting distributions. We also present a few important byproducts of their own\u003Cbr \/\u003E\r\ninterest, including a novel variant of the dynamic programming algorithm to\u003Cbr \/\u003E\r\nboost the computational efficiency, consistent change point localisation rates\u003Cbr \/\u003E\r\nunder functional dependence and a new Bernstein inequality for data possessing\u003Cbr \/\u003E\r\nfunctional dependence. \u0026nbsp;The paper is available at\u0026nbsp;\u003Ca href=\u0022http:\/\/arxiv.org\/abs\/2207.12453\u0022\u003Ehttp:\/\/arxiv.org\/abs\/2207.12453\u003C\/a\u003E\u003C\/p\u003E\r\n","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003E\u003Cstrong\u003EBio\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EI am a Reader in the Department of Statistics, University of Warwick and a Turing Fellow at the Alan Turing Institute, previously an Associate Professor in the University of Warwick, a Lecturer in the University of Bristol, a postdoc of Professor Richard Samworth and a graduate student of Professor Zhiliang Ying. I obtained my academic degrees from Fudan University (B.Sc. in Mathematics, June 2009 and Ph.D. in Mathematical Statistics, June 2013).\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EAbstract\u0026nbsp;\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EThis paper concerns about the limiting distributions of change point\u003Cbr \/\u003E\r\nestimators, in a high-dimensional linear regression time series context, where\u003Cbr \/\u003E\r\na regression object $(y_t, X_t) \\in \\mathbb{R} \\times \\mathbb{R}^p$ is observed\u003Cbr \/\u003E\r\nat every time point $t \\in \\{1, \\ldots, n\\}$. At unknown time points, called\u003Cbr \/\u003E\r\nchange points, the regression coefficients change, with the jump sizes measured\u003Cbr \/\u003E\r\nin $\\ell_2$-norm. We provide limiting distributions of the change point\u003Cbr \/\u003E\r\nestimators in the regimes where the minimal jump size vanishes and where it\u003Cbr \/\u003E\r\nremains a constant. We allow for both the covariate and noise sequences to be\u003Cbr \/\u003E\r\ntemporally dependent, in the functional dependence framework, which is the\u003Cbr \/\u003E\r\nfirst time seen in the change point inference literature. We show that a\u003Cbr \/\u003E\r\nblock-type long-run variance estimator is consistent under the functional\u003Cbr \/\u003E\r\ndependence, which facilitates the practical implementation of our derived\u003Cbr \/\u003E\r\nlimiting distributions. We also present a few important byproducts of their own\u003Cbr \/\u003E\r\ninterest, including a novel variant of the dynamic programming algorithm to\u003Cbr \/\u003E\r\nboost the computational efficiency, consistent change point localisation rates\u003Cbr \/\u003E\r\nunder functional dependence and a new Bernstein inequality for data possessing\u003Cbr \/\u003E\r\nfunctional dependence. \u0026nbsp;The paper is available at\u0026nbsp;\u003Ca href=\u0022http:\/\/arxiv.org\/abs\/2207.12453\u0022\u003Ehttp:\/\/arxiv.org\/abs\/2207.12453\u003C\/a\u003E\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":" Change point inference in high-dimensional regression models under temporal dependence"}],"uid":"36358","created_gmt":"2022-08-24 15:22:02","changed_gmt":"2022-08-24 15:23:42","author":"chumphrey30","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2022-10-06T13:00:00-04:00","event_time_end":"2022-10-06T14:00:00-04:00","event_time_end_last":"2022-10-06T14:00:00-04:00","gmt_time_start":"2022-10-06 17:00:00","gmt_time_end":"2022-10-06 18:00:00","gmt_time_end_last":"2022-10-06 18:00:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"78771","name":"Public"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}