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Biostatistics Seminars

Department of Biostatistics Seminar Series

The Department of Biostatistics presents a regular speaker series each semester, generally on Thursday afternoon each week. Diverse experts lecture on their work in biostatistics.

Upcoming Biostats Seminars

Thu 3/1/2018 3:30PM - 4:30PM
Inference and Variable Selection with Random Forests - Lucas Mentch
Public Health Auditorium (G23)

Biostatistics guest speaker, Lucas Mentch, University of Pittsburgh, Statistics, will present, "Inference and Variable Selection with Random Forests."
Thu 4/5/2018 3:30PM - 4:30PM
Seonjoo Lee, Columbia University
Public Health Auditorium (G23)

Biostatistics guest speaker, Seonjoo Lee, Columbia University, will present.
Thu 4/12/2018 3:30PM - 4:30PM
Ciprian Crainiceanu, Johns Hopkins University
Public Health Auditorium (G23)

Biostatistics guest speaker, Ciprian Crainiceanu, Johns Hopkins University, will present.
Thu 4/19/2018 3:30PM - 4:30PM
Wensheng Guo, University of Pennsylvania
Public Health Auditorium (G23)

Biostatistics guest speaker, Wensheng Guo, University of Pennsylvania, will present.

Previous Biostats Seminars

Biostatistics Seminar Series

Ali Shojaie, University of Washington - Flexibility in High Dimensions: Sparse Additive Models

Thursday 4/6 3:30PM - 4:30PM
Public Health Auditorium (G23)

Biostatistics Seminar speaker, Ali Shojaie, PhD, Associate Professor, Dept. of Biostatistics, University of Washington, will present, “Flexibility in High Dimensions: Sparse Additive Models with Adaptive Truncation via a Convex Hierarchical Penalty”.
ABSTRACT: We consider the problem of nonparametric regression with a potentially large number of covariates. We propose a convex penalized estimation framework that is particularly well-suited for high-dimensional sparse additive models. Existing sparse additive modeling approaches assume that all additive components have the same level of complexity and are thus not data-adaptive. In contrast, the proposed approach selects the appropriate level of complexity for each additive component data-adaptively . Importantly, this flexibility is achieved without sacrificing computational efficiency: We demonstrate that the proposed approach scales similarly to the LASSO with the number of covariates and samples size. We demonstrate these properties through empirical studies on both real and simulated datasets and show that our estimator converges at the minimax rate. 


Last Updated On Thursday, March 30, 2017 by Kapko, Bernadette E
Created On Monday, December 19, 2016


For information on seminars and events in the department, contact:

Bernadette Kapko

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