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DTSTART:20070311T020000
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DTSTAMP:20250911T132206
DTSTART;TZID=America/Detroit:20250910T120000
DTEND;TZID=America/Detroit:20250910T133000
SUMMARY:Workshop / Seminar:Medicine\, Aging\, Science & Health (MASH) Workshop
DESCRIPTION:- September 10: Abby-Lynn Smith\n- October 8: Liz Harris\n- October 15: Analidis Ochoa\n- October 22: Hsin-Keng Ling\n- October 29: Megan Kelly\n- November 6: Special Event - Society of Fellows lunch with Neil Gong (co-sponsored with ISD)\n- November 12: Sofia Hiltner\n- November 19: Renee Anspach
UID:139226-21885130@events.umich.edu
URL:https://events.umich.edu/event/139226
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Graduate Students
LOCATION:LSA Building - 4147
CONTACT:
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BEGIN:VEVENT
DTSTAMP:20250903T160500
DTSTART;TZID=America/Detroit:20250910T120000
DTEND;TZID=America/Detroit:20250910T130000
SUMMARY:Presentation:MPSDS JPSM Seminar Series - Where Granularity Matters: Calibrating Subdomain Inference for Binary Outcomes
DESCRIPTION:MPSDS JPSM Seminar Series\nMPSDS M3 Series\n\nSeptember 3\, 2025\n12:00 - 1:00 pm EST\n\nIn person\, room 1070\, Institute for Social Research and via Zoom.\nThe Zoom call will be locked 10 minutes after the start of the presentation.\n\nWhere Granularity Matters: Calibrating Subdomain Inference for Binary Outcomes\nSmall area estimation (SAE) helps us make accurate estimates for local communities or groups\, such as counties\, neighborhoods\, or demographic subgroups\, when there are not enough data for each area. This is important for targeting local resources and policies\, especially when national-level or large-area data mask variation at a more granular level. Researchers often fit hierarchical Bayesian models to stabilize estimates when data are sparse. Ideally\, Bayesian procedures also exhibit good frequentist properties\, as demonstrated by calibrated Bayes techniques. However\, hierarchical Bayesian models tend to shrink subdomain estimates toward the overall mean and may produce credible intervals that do not maintain nominal coverage. Hoff et al. developed the Frequentist\, but Assisted by Bayes (FAB) intervals for subgroup estimates with normally distributed outcomes. However\, non-normally distributed data present new challenges\, and multiple types of intervals have been proposed for estimating proportions. We examine subdomain inference with binary outcomes and extend FAB intervals to improve nominal coverage and estimation efficiency. We describe how to numerically compute FAB intervals in the binary case and demonstrate their improvement through repeated simulation studies. Finally\, we apply the proposed methods to estimate COVID-19 infection rates in subgroups\, based on geography and demographic characteristics. This is joint work with Rayleigh Lei.\n\nYajuan Si is a Research Associate Professor in the Institute for Social Research at the University of Michigan. Yajuan’s research lies in cutting-edge methodology development in streams of Bayesian statistics\, linking design- and model-based approaches to survey inference\, data integration\, missing data analysis\, confidentiality protection involving the creation and analysis of synthetic datasets\, and causal inference with observational data. She regularly teaches courses on statistics and sampling in the Michigan Program in Survey and Data Science and the Joint Program in Survey Methodology.
UID:138682-21883610@events.umich.edu
URL:https://events.umich.edu/event/138682
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Aging,Anthropology,Basic Science,Biosciences,brown bag,Climate and Space Sciences and Engineering,Data,Data Analysis,Data Collection,Data Curation,Data Linkage,Data Management,Data Science,Economics,Election,Electrical Engineering and Computer Science,Free,gerald r. ford school of public policy,Graduate,Graduate and Professional Students,Graduate Students,Health Data,Immigration,In Person,Information and Technology,Interdisciplinary,Lecture,Livestream,Mathematics,Multidisciplinary Design,Political Economy,Political Science,Politics,Population Studies Center,Psychology,Public Policy,Research,Science,Social Sciences,Sociology,Statistics,Survey Methodology,Survey Methods,Survey Research,Talk,Virtual
LOCATION:Off Campus Location
CONTACT:
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