All talks will be held in CAS 109 unless otherwise specified.
Fall 2016 Colloquiums
Oct. 27 (Thu) 2:00-3:00 Dr. Ananda Sen (School of Public Health, UMichigan)
"Competing Risks and Missing Cause of Failure: An Overview"
Abstract: The topic of analyzing time-to-event data where individual units are subject to multiple causes for the event occurrence has been well-studied for decades. A case that has received lion’s share of attention in this context is when the event results from the earliest onset of a cause, a framework traditionally dubbed as that of competing risks. Examples are replete in manufacturing applications, software development, clinical trials, social sciences and risk analysis. Research objectives in such instances range from understanding a component’s contribution towards the overall system failure to assessing the role of any prognostic factor on the component failure history in the presence of the competing causes. Earlier work in competing risks analysis utilized a series system (observing the minimum of several lifetimes) formulation in terms of latent event times. It is well known that such a formulation is fraught with the issue of identifiability, unless one can assume the different causes to act independently. Alternative formulation through cause-specific quantities attempt to formulate a model that has direct links to the observables and avoids imposing a dependence structure on the causes. In some situations the cause of system failure is not known exactly, but can be narrowed down to a subset of potential causes. This phenomenon, referred to as masking, is often the result of incomplete or partial information on the failures arising in destructive experiments. This is an expository talk on the nuances of analyzing competing risks data under possible masking of failure causes. We shall cover both single and recurrent event data in this context. In keeping with the wide applicability of the framework, the methodology will be illustrated using a warranty claim database for a fleet of automobiles as well as registry data on cause of death of patients diagnosed with breast cancer.
Nov. 10 (Thu) 2:00-3:00 Dr. Won Chang (Dept. of Mathematical Sciences, UCincinnati)
"Improving Ice Sheet Model Calibration Using Paleoclimate and Modern Data"
Abstract: Human-induced climate change may cause significant ice volume loss from the West Antarctic Ice Sheet (WAIS). Projections of ice volume change from ice-sheet models and corresponding future sea-level rise have large uncertainties due to poorly constrained input parameters. In most future applications to date, model calibration has utilized only modern or recent (decadal) observations, leaving input parameters that control the long-term behavior of WAIS largely unconstrained. Many paleo-observations are in the form of localized time series, while modern observations are non-Gaussian spatial data; combining information across these types poses non-trivial statistical challenges. Here we introduce a computationally efficient calibration approach that utilizes both modern and paleo-observations to generate better-constrained ice volume projections. Using fast emulators built upon principal component analysis and a reduced dimension calibration model, we can efficiently handle high-dimensional and non-Gaussian data. We apply our calibration approach to the PSU3D-ICE model which can realistically simulate long-term behavior of WAIS. Our results show that using paleo observations in calibration significantly reduces parametric uncertainty, resulting in sharper projections about the future state of WAIS. One benefit of using paleo observations is found to be that unrealistic simulations with overshoots in past ice retreat and projected future regrowth are eliminated.
Fall 2016 Masters Paper Presentations
Thursday, April 16th from 2:10-3:00 Dr. Peter Craigmile, (Ohio State University) "Wavelet-based estimation of the long memory parameter in Gaussian non-gappy and gappy time series.
Thursday, March 19th from 2:10-3:00 Dr. Sujay Datta, (University of Akron) "Graphical and Network Models in Bioinformatics".
Thursday, February 12th from 2:10-3:00 Dr. John Tuhao Chen, (Bowling Green State University) "Step-up Confidence Procedures for the Minimum Effective Dose of a Drug."
Tue, Apr 19 12:30-1:30 (CAS438) Dr. Craig L. Zirbel (Dept. of Math & Stats, Bowling Green) Matching RNA motif sequences to known 3D geometries.
Thur, Apr 28 2:00-3:00 Dr. Thaddeus Tarpey (Dept. of Math, Wright State U) Calling Models Wrong for the Wrong Reasons