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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

Biostatistics Seminar: Scott Bruce, Temple University

Thursday 9/21 3:30PM - 5:00PM
Public Health Auditorium (G23)


Biostatistics Seminar guest speaker, Scott Bruce, of Temple University, will present, "Conditional Adaptive Spectral Analysis of Nonstationary Biomedical Time Series"


Many studies of biomedical time series signals aim to measure the association between frequency-domain properties of time series and clinical and behavioral covariates. However, the time-varying dynamics of these associations are largely ignored due to a lack of methods that can assess the changing nature of the relationship through time. This article introduces a method for the simultaneous and automatic analysis of the association between the time-varying power spectrum and covariates, which we refer to as conditional adaptive Bayesian spectrum analysis (CABS). The procedure adaptively partitions the grid of time and covariate values into an unknown number of approximately stationary blocks and nonparametrically estimates local spectra within blocks through penalized splines. CABS is formulated in a fully Bayesian framework, in which the number and locations of partition points are random, and fit using reversible jump Markov chain Monte Carlo techniques. Estimation and inference averaged over the distribution of partitions allows for the accurate analysis of spectra with both smooth and abrupt changes. The proposed methodology is motivated by and used to analyze the association between the time-varying spectrum of heart rate variability (HRV) and self-reported sleep quality in a study of older adults serving as the primary caregiver for their ill spouse.

Last Updated On Tuesday, August 29, 2017 by Haydo, Amber LC
Created On Wednesday, August 09, 2017

Contact

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

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

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