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SUMMARY:Workshop / Seminar:Statistics Department Seminar Series: Fred Feinberg\, Handleman Professor of Management\, Ross School of Business\, Professor of Statistics (by courtesy)\, Department of Statistics\, University of Michigan
DESCRIPTION:Abstract: Expert adjudications are ubiquitous in high-stakes decision-making\, from grant reviews and academic hiring to elite evaluations in the arts and athletics. In these settings\, panels of judges score candidates across sequential stages\, and these scores are aggregated into a consensus ranking. Standard practice typically employs arithmetic averaging\, often supplemented with ad-hoc \"corrections\" for outliers or scale differences. However\, such approaches suffer from three core statistical problems: (1) Scale Heterogeneity\, where judges exhibit varying levels of discrimination and range-restriction\; (2) Information Loss\, where the longitudinal \"trajectory\" of a candidate is sidestepped in favor of stage-specific snapshots\; and (3) Nonignorable Missingness\, where conflict-of-interest (COI) recusals can introduce systematic bias.\n\nWe develop a hierarchical Bayesian framework that addresses these issues simultaneously. First\, we treat observed scores as generators of ordinal tie-blocks\, bypassing the \"cardinality fallacy\" and modeling the probability of observed ranks. Second\, we link sequential rounds via a fusion model with LKJ correlation priors\, allowing the model to borrow strength across the tournament while regularizing the latent covariance. Third\, we introduce a novel Informative Missing Data Likelihood (MDL) that treats COI recusals as a form of informative censoring. When judges abstain from rating their own students or collaborators\, standard approaches invoke a \"Missing Completely at Random\" (MCAR) assumption. Our MDL instead retains recused candidates in the \"risk set\" as censored alternatives\, correcting for the potential bias in win probabilities that occurs when high-caliber competitors are systematically excluded from a judge’s denominator. The model combines a Plackett–Luce formulation for tied data (implemented via Elementary Symmetric Polynomials) with judge-specific discrimination parameters that automatically downweight poorly-calibrated raters\, and the full posterior can be efficiently sampled via Hamiltonian Monte Carlo\, allowing full uncertainty quantification in downstream estimands.\n\nWe apply this framework to a high-stakes international competition — to be revealed during the talk! — featuring dozens of candidates\, multiple rounds\, and nearly 20 elite judges. Analysis suggests that the standard scoring method and the MDL-augmented model produce distinctly different results: they disagree on the winner and posterior advancement probabilities\, driven almost entirely by the differential treatment of collaborator-based recusals. Sensitivity analysis reveals that these outcomes are largely contingent on the assumed missing data mechanism. By making these untestable assumptions explicit\, we provide a more transparent and principled foundation for high-stakes adjudication in grant panels\, hiring committees\, and both athletic and artistic judging.
UID:144784-21895842@events.umich.edu
URL:https://events.umich.edu/event/144784
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:seminar
LOCATION:West Hall - 340
CONTACT:
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DTSTAMP:20251215T133743
DTSTART;TZID=America/Detroit:20260220T100000
DTEND;TZID=America/Detroit:20260220T143000
SUMMARY:Workshop / Seminar:Story Lab Winter Retreats
DESCRIPTION:ABOUT\nStory Lab develops executive-level presence and communication skills through storytelling workshops and events. To be an effective leader—at work\, in the community\, or in your personal life—you must be able to communicate with impact. Often this means telling stories that are meaningful to you and others\, and doing so in the rich language and expressive style of a seasoned storyteller. If you can craft and deliver an effective story\, you will be better able to convey your value to recruiters\, inspire and motivate classmates and colleagues\, and influence your audience. At Story Lab\, you’ll find an immersive experience and an opportunity to hone your skills in a safe and supportive environment.\n\nStory Lab is generously sponsored by M•LEAD and the Ford School’s Leadership Initiative.\n\nDATES\n2/18\, 4:30–9 PM @ the Michigan League OR 2/20\, 10 AM–2:30 PM (virtual) (Choose ONE)\nDevelop your storytelling abilities.\n\nPARTICIPANT REQUIREMENTS\nDeep interest in storytelling\, personal growth\, and lifelong learning. \nThis program is open to all U-M students. However\, because space is limited\, virtual offerings of Sanger programs will prioritize the registration of Online\, Weekend\, and Executive MBA students. Any remaining spots will be made available to students in other programs.\n\nREGISTRATION WINDOW\n2/2–2/13\n\nVisit our webpage to learn more!
UID:137303-21880097@events.umich.edu
URL:https://events.umich.edu/event/137303
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Graduate,Workshop,Undergraduate Students,Undergraduate,Storytelling,Leadership,Graduate Students,Free
LOCATION:Off Campus Location
CONTACT:
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