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UID:614@fds.yale.edu
DTSTART;TZID=America/New_York:20240424T113000
DTEND;TZID=America/New_York:20240424T130000
DTSTAMP:20250916T142134Z
URL:https://fds.yale.edu/events/fds-colloquium-david-matteson-cornell/
SUMMARY:FDS Colloquium: David Matteson (Cornell)\, “Drift vs Shift: Decou
 pling Trends and Changepoint Analysis”
DESCRIPTION:Webcast Link (via Zoom - starts at 12:00): https://yale.zoom.us
 /s/7859884026\n\n\n\nAbstract: We introduce a new approach for decoupling 
 trends (drift) and changepoints (shifts) in time series. Our locally adapt
 ive model-based approach for robustly decoupling combines Bayesian trend f
 iltering and machine learning based regularization. An over-parameterized 
 Bayesian dynamic linear model (DLM) is first applied to characterize drift
 . Then a weighted penalized likelihood estimator is paired with the estima
 ted DLM posterior distribution to identify shifts. We show how Bayesian DL
 Ms specified with so-called shrinkage priors can provide smooth estimates 
 of underlying trends in the presence of complex noise components. However\
 , their inability to shrink exactly to zero inhibits direct changepoint de
 tection. In contrast\, penalized likelihood methods are highly effective i
 n locating changepoints. However\, they require data with simple patterns 
 in both signal and noise. The proposed decoupling approach combines the st
 rengths of both\, i.e. the flexibility of Bayesian DLMs with the hard thre
 sholding property of penalized likelihood estimators\, to provide changepo
 int analysis in complex\, modern settings. The proposed framework is outli
 er robust and can identify a variety of changes\, including in mean and sl
 ope. It is also easily extended for analysis of parameter shifts in time-v
 arying parameter models like dynamic regressions. We illustrate the flexib
 ility and contrast the performance and robustness of our approach with sev
 eral alternative methods across a wide range of simulations and applicatio
 n examples.\n\n\n\nBio: David S. Matteson is Professor of Statistics & Dat
 a Science at Cornell University and Director of the National Institute of 
 Statistical Sciences (NISS.org) - the joint research unit of the American 
 Statistical Association (ASA)\, the International Biometric Society (IBS)\
 , the Institute of Mathematical Statistics (IMS). At Cornell\, he is also 
 Associate Department Chair of Statistics & Data Science\, Director of Grad
 uate Studies in Data Science\, and Founding Executive Committee Member for
  the Cornell Center for Data Science for Enterprise & Society. His researc
 h centers on methods and theory for modeling complex human and natural p
 rocesses and systems\, with expertise in dynamic\, spatial\, functional\, 
 and network data science and machine learning. He received a CAREER Award 
 from the National Science Foundation (NSF)\, the Chancellor’s Award for 
 Scholarship and Creative Activities from the State University of New York\
 , the inaugural Ann S. Bowers Research Excellence Award\, and Faculty Rese
 arch Awards from the Xerox/PARC Foundation and LinkedIn. He has served as 
 lead PI and Director for several NSF funded collaborative institutes\, and
  he founded and serves as Editor-in-Chief for the new open access ASA affi
 liated journal Data Science in Science.\n\n\n\nWebsite: https://davidsmatt
 eson.com/\n\n\n\n\n\n\n\n\n
ATTACH;FMTTYPE=image/jpeg:https://fds.yale.edu/wp-content/uploads/2024/03/
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CATEGORIES:FDS Events,Colloquium,Seminar Series
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