{"181691":{"#nid":"181691","#data":{"type":"event","title":"Statistics Seminar","body":[{"value":"\u003Cp\u003ETITLE: Nonparametric Maximum Likelihood Approach to Multiple Change-Point Problems\u003C\/p\u003E\u003Cp\u003ESPEAKER: Changliang Zou (visiting Dr. Jeff Wu)\u003C\/p\u003E\u003Cp\u003EABSTRACT:\u003C\/p\u003E\u003Cp\u003EIn multiple change-point problems, different data segments follow different distributions where changes may be in the mean, scale or the entire distribution from one segment to another. Without the need to know the number of change-points in advance, in this talk I will introduce a nonparametric maximum likelihood approach to detecting multiple change-points. Our method does not impose any parametric assumption on the underlying distributions of the data \u003Cbr \/\u003Esequence, which is thus suitable for detection of any changes in the distributions. The number of change-points is determined by the Bayesian information criterion and the locations of the \u003Cbr \/\u003Echange-points can be estimated by using the dynamic programming algorithm and further taking advantage of the intrinsic order structure of the likelihood function. Under some mild conditions, we show that the new method provides consistent estimation for both the \u003Cbr \/\u003Elocations and magnitudes of the change-points with a rate, the square of log n. We also suggest a pre-screening procedure which is capable of excluding most of the irrelevant points. Simulation studies show that the proposed method has outstanding performance of \u003Cbr \/\u003Eidentifying multiple change-points in terms of estimation accuracy and computation time compared with existing methods. The new methodology is illustrated with two real data examples. \u003Cbr \/\u003E \u003Cbr \/\u003EContact: Changliang ZOU \u003Ca class=\u0022moz-txt-link-rfc2396E\u0022 href=\u0022mailto:chlzou@yahoo.com.cn\u0022\u003E\u0026lt;chlzou@yahoo.com.cn\u0026gt;\u003C\/a\u003E\u003C\/p\u003E","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":"","field_summary_sentence":[{"value":"Statistics Seminar"}],"uid":"27187","created_gmt":"2013-01-09 08:07:30","changed_gmt":"2016-10-08 02:01:55","author":"Anita Race","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2013-04-09T12:00:00-04:00","event_time_end":"2013-04-09T13:00:00-04:00","event_time_end_last":"2013-04-09T13:00:00-04:00","gmt_time_start":"2013-04-09 16:00:00","gmt_time_end":"2013-04-09 17:00:00","gmt_time_end_last":"2013-04-09 17:00:00","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":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003EDr. Jeff Wu\u003C\/p\u003E\u003Cp\u003E\u003Ca href=\u0022mailto:jeff.wu@isye.gatech.edu\u0022\u003Ejeff.wu@isye.gatech.edu\u003C\/a\u003E\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}}}