{"43420":{"#nid":"43420","#data":{"type":"event","title":"Generalized and Robust Nonparametric Regression","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETITLE:\u003C\/strong\u003E  Generalized and Robust Nonparametric Regression\n\u003C\/p\u003E\n\u003Cp\u003E\u003Cstrong\u003ESPEAKER:\u003C\/strong\u003E  Tony Cai\u003Cbr \/\u003E\nDepartment of Statistics\u003Cbr \/\u003E\nThe Wharton School\u003Cbr \/\u003E\nUniversity of Pennsylvania\n\u003C\/p\u003E\n\u003Cp\u003E\u003Cstrong\u003EABSTRACT:\u003C\/strong\u003E\n\u003C\/p\u003E\n\u003Cp\u003EMuch of the nonparametric regression theory is focused on the case of additive Gaussian noise. In such a setting many smoothing techniques including wavelet thresholding methods have been developed and shown to be highly adaptive. \n\u003C\/p\u003E\n\u003Cp\u003EIn this talk we consider robust nonparametric regression, where the noise distribution is unknown and possibly heavy-tailed, and generalized nonparametric regression in exponential families which include, for example, Poisson regression, binomial regression, and Gamma regression. We take a unified approach of using a transformation to convert each of these problems into a standard homoskedastic Gaussian regression problem.   Then in principle any good nonparametric Gaussian regression procedurecan be applied to the transformed data. We use a wavelet block thresholding procedure to illustrate our method and show that the resulting estimators are adaptively rate-optimal over a range of Besov Spaces. The procedure is easily implementable.  A key technical step is the development of a quantile coupling theorem that is used to connect our problem with a more familiar Gaussian setting.\u003C\/p\u003E","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"Generalized and Robust Nonparametric Regression","format":"limited_html"}],"field_summary_sentence":[{"value":"Generalized and Robust Nonparametric Regression"}],"uid":"27187","created_gmt":"2009-10-12 20:38:07","changed_gmt":"2016-10-08 01:47:38","author":"Anita Race","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2008-10-17T12:00:00-04:00","event_time_end":"2008-10-17T13:00:00-04:00","event_time_end_last":"2008-10-17T13:00:00-04:00","gmt_time_start":"2008-10-17 16:00:00","gmt_time_end":"2008-10-17 17:00:00","gmt_time_end_last":"2008-10-17 17:00:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[{"id":"5730","name":"Nonparametric regression"}],"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":"\u003Cstrong\u003EAnita  Race\u003C\/strong\u003E\u003Cbr \/\u003EH. Milton Stewart School of Industrial and Systems Engineering\u003Cbr \/\u003E\u003Ca href=\u0022http:\/\/www.gatech.edu\/contact\/index.html?id=ar9\u0022\u003EContact Anita  Race\u003C\/a\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}}}