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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

Xiaoxiao Sun, Univ. of Georgia on Smoothing Parameters for large Smoothing Spline ANOVA Models

Thursday 1/11 3:30PM - 4:30PM
Public Health Auditorium (G23)

Biostatistics guest speaker, Xiaoxiao Sun, University of Georgia, will present, "Theory Informs Practice: Smoothing Parameters Selection for Smoothing Spline ANOVA Models in Large Samples."  

Large samples have been generated routinely from various sources. Classic statistical models, such as smoothing spline ANOVA models, are not well equipped to analyze such large samples due to expensive computational costs. In particular, the daunting computational costs of selecting smoothing parameters render the smoothing spline ANOVA models impractical. In this talk, I will present an asympirical (asymptotic + empirical) smoothing parameters selection approach for smoothing spline ANOVA models in large samples. The proposed method can significantly reduce computational costs of selecting smoothing parameters in high-dimensional and large-scale data. We show smoothing parameters chosen by the proposed method tend to the optimal smoothing parameters minimizing a risk function. In addition, the estimator based on the proposed smoothing parameters achieves the optimal convergence rate. Extensive simulation studies will be presented to demonstrate numerical advantages of our method over competing methods. I will further illustrate the empirical performance of theproposed approach using real data.

Last Updated On Friday, January 12, 2018 by Borkowski, Matthew Gerard
Created On Friday, January 05, 2018

Contact

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

Bernadette Kapko
bkapko@pitt.edu
412-624-3022

© 2018 by University of Pittsburgh Graduate School of Public Health

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