{"61466":{"#nid":"61466","#data":{"type":"event","title":"High dimensional inverse covariance matrix estimation","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETITLE: \u003C\/strong\u003EHigh dimensional inverse covariance matrix estimation\n\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ESPEAKER:\u003C\/strong\u003E\u0026nbsp; Ming Yuan\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EABSTRACT:\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EMore and more often in practice, one needs to estimate a high\ndimensional covariance matrix. In this talk, we discuss how this task\nis often related to the sparsity of the inverse covariance matrix. In\nparticular, we consider estimating a (inverse) covariance matrix that\ncan be well approximated by ``sparse\u0027\u0027 matrices. Taking advantage of\nthe connection between multivariate linear regression and entries of\nthe inverse covariance matrix, we introduce an estimating procedure\nthat can effectively exploit such ``sparsity\u0027\u0027.\u0026nbsp; The proposed method\ncan be computed using linear programming and therefore has the\npotential to be used in very high dimensional problems. Oracle\ninequalities are established for the estimation error in terms of\nseveral operator norms, showing that the method is adaptive to\ndifferent types of sparsity of the problem.\u003C\/p\u003E","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":"","field_summary_sentence":[{"value":"High dimensional inverse covariance matrix estimation"}],"uid":"27187","created_gmt":"2010-10-06 09:15:41","changed_gmt":"2016-10-08 01:52:31","author":"Anita Race","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2010-10-07T12:00:00-04:00","event_time_end":"2010-10-07T13:00:00-04:00","event_time_end_last":"2010-10-07T13:00:00-04:00","gmt_time_start":"2010-10-07 16:00:00","gmt_time_end":"2010-10-07 17:00:00","gmt_time_end_last":"2010-10-07 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":[],"invited_audience":[],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}