Statistics Weekly Seminar Series: Bertrand Clarke
Online Prediction with Streaming Observational Data
3:00 pm –
4:00 pm
Seminar Room
Room: HARH 228
Target Audiences:
3310 Holdrege Street
Lincoln NE 68583-0963
Lincoln NE 68583-0963
Contact:
Department of Statistics, statistics@unl.edu
Abstract:
The automated collection of streaming observational data has become standard and defies most traditional analytic techniques. It is not just that models are hard to identify, there may not be any model that can be This talk will review some of the most successful recent predictive methods for the M-Open problem class. Techniques include the predictors using expert advice such as the Shtarkov solution, Bayesian non-parametrics such as Gaussian process priors, and hash function based predictors. Throughout, the properties of the predictors are presented and compared from a principled standpoint. Safely and usefully assumed - indeed, frequently - it is only predictions that can be made and assessed. Problems for this kind of data are often called M-Open and have motivated new approaches and philosophies.
About the Speaker:
Bertrand Clarke earned his PhD in Statistics at the University of Illinois-Champaign-Urbana in 1989.His thesis work was given the Browder J. Thompson award for authors under age 30 of papers in IEEE journals. He spent three years as an Assistant Professor at Purdue University before moving to the University of British Columbia where he worked from 1992-2008. His early research focused on asymptotics, prior selection in Bayesian statistics, and mathematical modeling of biological systems. His first sabbatical was at University College London and his second sabbatical was at Duke University where he was a visiting scholar in the `Large P Small N’ program at SAMSI. In addition, in 2008 he spent three months at the Newton Institute at Cambridge University. He moved to the University of Miami in 2008 and worked for five years at the medical school where he started their MS and PhD programs in biostatistics before coming to Chair the Department of Statistics at the University of Nebraska-Lincoln. His current foci of research are predictive statistics and statistical methodology in genomic data. He has been an associate editor for four different journals, served three years on the Savage Award Committee (best thesis prize in Bayesian statistics), has published numerous papers over several fields, and was made a Fellow of the ASA in 2014. He has also authored one PhD level textbook on data mining and machine learning for Springer, with a complete solutions manual (available to instructors on request).
The automated collection of streaming observational data has become standard and defies most traditional analytic techniques. It is not just that models are hard to identify, there may not be any model that can be This talk will review some of the most successful recent predictive methods for the M-Open problem class. Techniques include the predictors using expert advice such as the Shtarkov solution, Bayesian non-parametrics such as Gaussian process priors, and hash function based predictors. Throughout, the properties of the predictors are presented and compared from a principled standpoint. Safely and usefully assumed - indeed, frequently - it is only predictions that can be made and assessed. Problems for this kind of data are often called M-Open and have motivated new approaches and philosophies.
About the Speaker:
Bertrand Clarke earned his PhD in Statistics at the University of Illinois-Champaign-Urbana in 1989.His thesis work was given the Browder J. Thompson award for authors under age 30 of papers in IEEE journals. He spent three years as an Assistant Professor at Purdue University before moving to the University of British Columbia where he worked from 1992-2008. His early research focused on asymptotics, prior selection in Bayesian statistics, and mathematical modeling of biological systems. His first sabbatical was at University College London and his second sabbatical was at Duke University where he was a visiting scholar in the `Large P Small N’ program at SAMSI. In addition, in 2008 he spent three months at the Newton Institute at Cambridge University. He moved to the University of Miami in 2008 and worked for five years at the medical school where he started their MS and PhD programs in biostatistics before coming to Chair the Department of Statistics at the University of Nebraska-Lincoln. His current foci of research are predictive statistics and statistical methodology in genomic data. He has been an associate editor for four different journals, served three years on the Savage Award Committee (best thesis prize in Bayesian statistics), has published numerous papers over several fields, and was made a Fellow of the ASA in 2014. He has also authored one PhD level textbook on data mining and machine learning for Springer, with a complete solutions manual (available to instructors on request).
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This event originated in Statistics Seminar.