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CATEGORIES:Main CONTACT;X-BEDEWORK-UID=8a00ec8b-979413b9-0198-a4d89b28-00001675:Thompson\, Ekaterina CREATED:20250904T132242Z DESCRIPTION:TBD DURATION:PT1H DTSTAMP:20251119T160701Z DTSTART;TZID=America/New_York:20250912T153000 LAST-MODIFIED:20251119T160701Z LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi stry 116 STATUS:CONFIRMED SUMMARY:Friday Seminar in the department of Statistical Science UID:CAL-8a00ec8b-979413b9-0199-14e4ca9c-0000776cdemobedework@mysite.edu X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-SPEAKER:TBD X-BEDEWORK-DUKE-SERIES:Friday Seminar in the department of Statistical Sci ence X-BEDEWORK-SUBMITTEDBY:ek217 for Statistical Science (agrp_StatisticalScie nce) RRULE:FREQ=WEEKLY;INTERVAL=1;UNTIL=20251122T050000Z EXDATE:20251010T193000Z END:VEVENT BEGIN:VEVENT RECURRENCE-ID;TZID=America/New_York:20250912T153000 CATEGORIES:Utilities CATEGORIES:Main CONTACT;X-BEDEWORK-UID=8a00ec8b-979413b9-0198-a4d89b28-00001675:Thompson\, Ekaterina CREATED:20250904T132242Z DESCRIPTION:Conditional moment equality models are regularly encountered i n empirical economics\, yet they are difficult to estimate. These models map a conditional distribution of data to a structural parameter via the restriction that a conditional mean equals zero. Using this observation\, I introduce a Bayesian inference framework in which an unknown condition al distribution is replaced with a nonparametric posterior\, and structur al parameter inference is then performed using an implied posterior. The method has the same flexibility as frequentist semiparametric estimators and does not require converting conditional moments to unconditional mome nts. Importantly\, I prove a semiparametric Bernstein-von Mises theorem\, providing conditions under which\, in large samples\, the posterior for the structural parameter is approximately normal\, centered at an efficie nt estimator\, and has variance equal to the Chamberlain (1987) semiparam etric efficiency bound. As byproducts\, I show that Bayesian uncertainty quantification methods are asymptotically optimal frequentist confidence sets and derive low-level sufficient conditions for Gaussian process prio rs. The latter sheds light on a key prior stability condition and relates to the numerical aspects of the paper in which these priors are used to predict the welfare effects of price changes. DURATION:PT1H DTSTAMP:20250908T131546Z DTSTART;TZID=America/New_York:20250912T153000 LAST-MODIFIED:20250908T131546Z LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi stry 116 STATUS:CONFIRMED SUMMARY:Semiparametric Bayesian Inference for a Conditional Moment Equalit y Model UID:CAL-8a00ec8b-979413b9-0199-14e4ca9c-0000776cdemobedework@mysite.edu X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-SPEAKER:Christopher D. Walker X-BEDEWORK-DUKE-SERIES:Friday Seminar in the department of Statistical Sci ence X-BEDEWORK-SUBMITTEDBY:ek217 for Statistical Science (agrp_StatisticalScie nce) X-BEDEWORK-IMAGE-X1:12 X-BEDEWORK-IMAGE-Y1:110 X-BEDEWORK-IMAGE-X2:1268 X-BEDEWORK-IMAGE-Y2:947.3333333333334 X-BEDEWORK-IMAGE-CROP-WIDTH:1256 X-BEDEWORK-IMAGE-CROP-HEIGHT:837.3333333333334 X-BEDEWORK-IMAGE-ALT-TEXT:Christopher D. Walker\, Assistant Professor in t he Department of Economics at Duke University. X-BEDEWORK-IMAGE:/public/Images/Chris Walker-240827-0646_20250908011258PM. jpg X-BEDEWORK-THUMB-IMAGE:/public/Images/Chris Walker-240827-0646_20250908011 258PM-thumb.png END:VEVENT BEGIN:VEVENT RECURRENCE-ID;TZID=America/New_York:20250919T153000 CATEGORIES:Utilities CATEGORIES:Main CONTACT;X-BEDEWORK-UID=8a00ec8b-979413b9-0198-a4d89b28-00001675:Thompson\, Ekaterina CREATED:20250904T132242Z DESCRIPTION:Markov chain Monte Carlo (MCMC) algorithms are foundational me thods for Bayesian statistics. However\, most MCMC algorithms are inheren tly sequential\, and their time complexity scales linearly with the numbe r of samples generated. Previous work on adapting MCMC to modern hardware has therefore focused on running many independent chains in parallel. We take an alternative approach: we propose algorithms to evaluate MCMC sam plers in parallel across the chain length. To do this\, we build on recen t methods for parallel evaluation of nonlinear recursions that formulate the state sequence as a solution to a fixed-point problem\, which can be obtained via a parallel form of Newton's method. We show how this approac h can be used to parallelize Gibbs\, Metropolis-adjusted Langevin\, and H amiltonian Monte Carlo sampling across the sequence length. Moreover\, we prove theoretical results that link the convergence rate of the fixed-po int algorithm to the stability of the recursion\, yielding sublinear time complexity for certain problems. Finally\, to lower memory costs and red uce runtime in practice\, we develop quasi-Newton methods that are genera lly applicable for parallel evaluation of nonlinear recursions. Across s everal examples\, we demonstrate the simulation of up to hundreds of thou sands of MCMC samples with only tens of parallel Newton iterations. We fi nd that the proposed parallel algorithms accelerate MCMC\, in some cases by more than an order of magnitude compared to sequential evaluation. DURATION:PT1H DTSTAMP:20250915T131047Z DTSTART;TZID=America/New_York:20250919T153000 LAST-MODIFIED:20250915T131047Z LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi stry 116 STATUS:CONFIRMED SUMMARY:Parallelizing MCMC Across the Sequence Length: T samples in O(log2 T) time UID:CAL-8a00ec8b-979413b9-0199-14e4ca9c-0000776cdemobedework@mysite.edu X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-SPEAKER:Scott W. Linderman\, Assistant Professor of Statistics and an Institute Scholar in the Wu Tsai Neurosciences Institute at Stanfo rd University X-BEDEWORK-DUKE-SERIES:Friday Seminar in the department of Statistical Sci ence X-BEDEWORK-SUBMITTEDBY:ek217 for Statistical Science (agrp_StatisticalScie nce) X-BEDEWORK-IMAGE-X1:126 X-BEDEWORK-IMAGE-Y1:129 X-BEDEWORK-IMAGE-X2:897 X-BEDEWORK-IMAGE-Y2:643 X-BEDEWORK-IMAGE-CROP-WIDTH:771 X-BEDEWORK-IMAGE-CROP-HEIGHT:514 X-BEDEWORK-IMAGE-ALT-TEXT:Scott W. Linderman X-BEDEWORK-IMAGE:/public/Images/Scott_20250915011047PM.png X-BEDEWORK-THUMB-IMAGE:/public/Images/Scott_20250915011047PM-thumb.png END:VEVENT BEGIN:VEVENT RECURRENCE-ID;TZID=America/New_York:20250926T153000 CATEGORIES:Utilities CATEGORIES:Main CONTACT;X-BEDEWORK-UID=8a00ec8b-979413b9-0198-a4d89b28-00001675:Thompson\, Ekaterina CREATED:20250904T132242Z DESCRIPTION:Every weighted\, undirected graph describes a random walk on i ts vertex set\, which is reversible Markov chain. I will describe recent work on graph joinings\, which leverages this connection and ideas from optimal transport to gain insights into both graph isomorphisms and coupl ings of Markov chains. A joining of two graphs is a product graph that d escribes a reversible coupling of their random walks. Given two graphs wi th labeled vertices\, the optimal graph joining (OGJ) problem identifies a joining that minimizes the total weight of vertex pairs with different labels. I will present new results showing that\, for suitable families of labeled graphs\, OGJ can detect and identify isomorphisms between grap hsin the family. In the opposite direction\, the study of joinings leads naturally to a notion of disjointness that quantifies structural discorda nce between graphs. I will present a simple characterization of weak dis jointness\, and show how this yields new insights into reversible couplin gs of reversible Markov chains.\n\nJoint work with Yang Xiang\, Phuong Ho ang\, Bongsoo Yi\, and Kevin McGoff. DURATION:PT1H DTSTAMP:20250922T122008Z DTSTART;TZID=America/New_York:20250926T153000 LAST-MODIFIED:20250922T122008Z LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi stry 116 STATUS:CONFIRMED SUMMARY:Graph Joinings and Reversible Markov Chains UID:CAL-8a00ec8b-979413b9-0199-14e4ca9c-0000776cdemobedework@mysite.edu X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-SPEAKER:Andrew B. Nobel\, PhD X-BEDEWORK-DUKE-SERIES:Friday Seminar in the department of Statistical Sci ence X-BEDEWORK-SUBMITTEDBY:ek217 for Statistical Science (agrp_StatisticalScie nce) X-BEDEWORK-IMAGE-X1:0 X-BEDEWORK-IMAGE-Y1:55 X-BEDEWORK-IMAGE-X2:1230 X-BEDEWORK-IMAGE-Y2:875 X-BEDEWORK-IMAGE-CROP-WIDTH:1230 X-BEDEWORK-IMAGE-CROP-HEIGHT:820 X-BEDEWORK-IMAGE-ALT-TEXT:Andrew B. Nobel\, PhD X-BEDEWORK-IMAGE:/public/Images/Screenshot 2025-09-22 at 8.18.44 AM_202509 22122009PM.png X-BEDEWORK-THUMB-IMAGE:/public/Images/Screenshot 2025-09-22 at 8.18.44 AM_ 20250922122009PM-thumb.png END:VEVENT BEGIN:VEVENT RECURRENCE-ID;TZID=America/New_York:20251003T153000 CATEGORIES:Utilities CATEGORIES:Main CONTACT;X-BEDEWORK-UID=8a00ec8b-979413b9-0198-a4d89b28-00001675:Thompson\, Ekaterina CREATED:20250904T132242Z DESCRIPTION:In recent years\, longitudinal studies increasingly collect da ta where the primary measurements are functions or surfaces observed repe atedly over time. This talk introduces parsimonious modeling frameworks f or such functional data\, designed to extract meaningful low-dimensional features while respecting the longitudinal design. The methodology is com putationally efficient and well-suited for characterizing the dynamic evo lution of the underlying process. We then extend the framework to accommo date pointwise skewness in the data\, broadening its applicability. Build ing on the key ideas\, we discuss inference in the form of significance t ests for hypotheses of scientific interest in this setting. We conclude b y highlighting several open challenges and emerging directions in the ana lysis of longitudinal functional data. DURATION:PT1H DTSTAMP:20250929T132604Z DTSTART;TZID=America/New_York:20251003T153000 LAST-MODIFIED:20250929T132604Z LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi stry 116 STATUS:CONFIRMED SUMMARY:Longitudinal Functional Data Methods for Emerging Repeated Measure ments UID:CAL-8a00ec8b-979413b9-0199-14e4ca9c-0000776cdemobedework@mysite.edu X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-SPEAKER: Ana-Maria Staicu X-BEDEWORK-DUKE-SERIES:Friday Seminar in the department of Statistical Sci ence X-BEDEWORK-SUBMITTEDBY:ek217 for Statistical Science (agrp_StatisticalScie nce) X-BEDEWORK-IMAGE-X1:0 X-BEDEWORK-IMAGE-Y1:141 X-BEDEWORK-IMAGE-X2:1149 X-BEDEWORK-IMAGE-Y2:907 X-BEDEWORK-IMAGE-CROP-WIDTH:1149 X-BEDEWORK-IMAGE-CROP-HEIGHT:766 X-BEDEWORK-IMAGE-ALT-TEXT:Ana-Maria Staicu X-BEDEWORK-IMAGE:/public/Images/ana-maria_20250929012604PM.png X-BEDEWORK-THUMB-IMAGE:/public/Images/ana-maria_20250929012604PM-thumb.png END:VEVENT BEGIN:VEVENT RECURRENCE-ID;TZID=America/New_York:20251017T153000 CATEGORIES:Utilities CATEGORIES:Main CONTACT;X-BEDEWORK-UID=8a00ec8b-979413b9-0198-a4d89b28-00001675:Thompson\, Ekaterina CREATED:20250904T132242Z DESCRIPTION:Beta regression is used routinely for continuous proportional data\, but it often encounters practical issues such as a lack of robustn ess to misspecification of the beta distribution and sensitivity to outli ers. We develop an improved class of generalized linear models starting w ith the continuous binomial (cobin) distribution and further extending to dispersion mixtures of cobin distributions (micobin). The proposed cobin regression and micobin regression models have attractive robustness\, co mputation\, and flexibility properties. A key innovation is the Kolmogoro v-Gamma data augmentation scheme\, which facilitates Gibbs sampling for B ayesian computation\, including in hierarchical cases involving nested\, longitudinal\, or spatial data. We demonstrate robustness\, ability to ha ndle responses exactly at the boundary (0 or 1)\, and computational effic iency relative to beta regression in simulation experiments and through a nalysis of the benthic macroinvertebrate multimetric index of US lakes us ing lake watershed covariates.\n\nJoint work with Benjamin Dahl (Duke U)\ , Otso Ovaskainen (U Jyväskylä)\, David Dunson (Duke U). DURATION:PT1H DTSTAMP:20251014T120547Z DTSTART;TZID=America/New_York:20251017T153000 LAST-MODIFIED:20251014T120547Z LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi stry 116 STATUS:CONFIRMED SUMMARY:Scalable and robust regression models for continuous proportional data UID:CAL-8a00ec8b-979413b9-0199-14e4ca9c-0000776cdemobedework@mysite.edu X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-SPEAKER:Changwoo lee X-BEDEWORK-DUKE-SERIES:Friday Seminar in the department of Statistical Sci ence X-BEDEWORK-SUBMITTEDBY:ek217 for Statistical Science (agrp_StatisticalScie nce) X-BEDEWORK-IMAGE-X1:26 X-BEDEWORK-IMAGE-Y1:47 X-BEDEWORK-IMAGE-X2:556 X-BEDEWORK-IMAGE-Y2:400.3333333333333 X-BEDEWORK-IMAGE-CROP-WIDTH:530 X-BEDEWORK-IMAGE-CROP-HEIGHT:353.3333333333333 X-BEDEWORK-IMAGE-ALT-TEXT:Changwoo Lee X-BEDEWORK-IMAGE:/public/Images/Screenshot 2025-10-14 at 8.05.17 AM_202510 14120547PM.png X-BEDEWORK-THUMB-IMAGE:/public/Images/Screenshot 2025-10-14 at 8.05.17 AM_ 20251014120547PM-thumb.png END:VEVENT BEGIN:VEVENT RECURRENCE-ID;TZID=America/New_York:20251024T153000 CATEGORIES:Utilities CATEGORIES:Main CONTACT;X-BEDEWORK-UID=8a00ec8b-979413b9-0198-a4d89b28-00001675:Thompson\, Ekaterina CREATED:20250904T132242Z DESCRIPTION:We present a unified offline decision making framework. In the first part\, we consider a class of assortment optimization problems in an offline data-driven setting. A firm does not know the underlying custo mer choice model but has access to an offline dataset consisting of the h istorically offered assortment set\, customer choice\, and revenue. The o bjective is to use the offline dataset to find an optimal assortment. Due to the combinatorial nature of assortment optimization\, the problem of insufficient data coverage is likely to occur in the offline dataset. The refore\, designing a provably efficient offline learning algorithm become s a significant challenge. To this end\, we propose an algorithm referred as Pessimistic ASsortment opTimizAtion (PASTA) following the spirit of p essimism. We show the algorithm identifies the optimal assortment by only requiring the offline data to cover the optimal assortment under general settings. In particular\, we establish a regret bound for the offline as sortment optimization problem under the celebrated multinomial logit mode l and its generalizations\, where the regret is shown to be minimax optim al. Joint work with Juncheng Dong\, Weibin Mo\, Zhengling Qi\, Cong Shi\, and Vahid Tarokh.\n \nIn the second part\, we consider the inferential p roblem in assortment optimization. Uncertainty quantification for the opt imal assortment is still largely unexplored despite its great practical significance. Instead of estimating and recovering the complete optimal o ffer set\, decision-makers may only be interested in testing whether a gi ven property holds true for the optimal assortment\, such as whether they should include several products of interest in the optimal set\, or how many categories of products the optimal set should include. We proposes a novel inferential framework for testing such properties. We reduce infer ring a general optimal assortment property to quantifying the uncertainty associated with the sign change point detection of the marginal revenue gaps. We show the asymptotic normality of the marginal revenue gap estima tor\, and construct a maximum statistic via the gap estimators to detect the sign change point. Joint work with Shuting Shen\, Alex Belloni\, Xi C hen\, and Junwei Lu. DURATION:PT1H30M DTSTAMP:20251023T162033Z DTSTART;TZID=America/New_York:20251024T150500 LAST-MODIFIED:20251023T162033Z LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi stry 116 STATUS:CONFIRMED SUMMARY:Offline Data-Driven Decision Making with Applications to Assortmen t Optimization: Estimation and Inference UID:CAL-8a00ec8b-979413b9-0199-14e4ca9c-0000776cdemobedework@mysite.edu X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-SPEAKER:Ethan Fang X-BEDEWORK-DUKE-SERIES:Friday Seminar in the department of Statistical Sci ence X-BEDEWORK-SUBMITTEDBY:ek217 for Statistical Science (agrp_StatisticalScie nce) X-BEDEWORK-IMAGE-X1:0 X-BEDEWORK-IMAGE-Y1:183 X-BEDEWORK-IMAGE-X2:2366 X-BEDEWORK-IMAGE-Y2:1760.3333333333333 X-BEDEWORK-IMAGE-CROP-WIDTH:2366 X-BEDEWORK-IMAGE-CROP-HEIGHT:1577.3333333333333 X-BEDEWORK-IMAGE-ALT-TEXT:Ethan Fang X-BEDEWORK-IMAGE:/public/Images/Ethan_Fang_Photo_20251020052429PM.jpg X-BEDEWORK-THUMB-IMAGE:/public/Images/Ethan_Fang_Photo_20251020052429PM-th umb.png END:VEVENT BEGIN:VEVENT RECURRENCE-ID;TZID=America/New_York:20251031T153000 CATEGORIES:Utilities CATEGORIES:Main CONTACT;X-BEDEWORK-UID=8a00ec8b-979413b9-0198-a4d89b28-00001675:Thompson\, Ekaterina CREATED:20250904T132242Z DESCRIPTION:With increased data collection\, the need to fuse data sources has emerged as an important and rapidly growing research activity in the statistical community. In considering spatial and spatio-temporal datase ts to examine complex environmental and ecological processes of interest\ , we often have multiple sources that are jointly informative about featu res of interest of the processes. Model-based data fusion aims to leverag e information from these sources to improve inference and prediction. In the spatial statistics setting\, these data could be geostatistical\, are al\, or point patterns with varying spatial resolutions\, supports\, and domains. With focus on North Atlantic right whales\, we explore stochasti c modeling to implement a suitable fusion to inform about their abundance and distribution with full inference and uncertainty. The first source i s aerial distance sampling\, which provides the spatial locations of whal es detected in the region. The second source is passive acoustic monitori ng (PAM)\, returning calls received at hydrophones placed on the ocean fl oor. Due to limited time on the surface and detection limitations arising from sampling effort\, aerial distance sampling only provides a partial realization of locations. With PAM we never observe numbers or locations of individuals. To address these challenges\, we develop a novel thinned point pattern data fusion. We demonstrate performance gains of our approa ch compared to that from a single source through simulation and apply our model to North Atlantic right whale data collected throughout Cape Cod B ay\, Massachusetts in the U.S. DURATION:PT1H DTSTAMP:20251028T164557Z DTSTART;TZID=America/New_York:20251031T153000 LAST-MODIFIED:20251028T164557Z LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi stry 116 STATUS:CONFIRMED SUMMARY:Spatial data fusion of point processes to infer marine mammal abun dance UID:CAL-8a00ec8b-979413b9-0199-14e4ca9c-0000776cdemobedework@mysite.edu X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-SPEAKER:Erin Schliep X-BEDEWORK-DUKE-SERIES:Friday Seminar in the department of Statistical Sci ence X-BEDEWORK-SUBMITTEDBY:ek217 for Statistical Science (agrp_StatisticalScie nce) X-BEDEWORK-IMAGE-X1:52 X-BEDEWORK-IMAGE-Y1:112 X-BEDEWORK-IMAGE-X2:1050 X-BEDEWORK-IMAGE-Y2:777.3333333333334 X-BEDEWORK-IMAGE-CROP-WIDTH:998 X-BEDEWORK-IMAGE-CROP-HEIGHT:665.3333333333334 X-BEDEWORK-IMAGE-ALT-TEXT:Erin Schliep X-BEDEWORK-IMAGE:/public/Images/Screenshot 2025-10-27 at 1.31.57 PM_202510 28044557PM.png X-BEDEWORK-THUMB-IMAGE:/public/Images/Screenshot 2025-10-27 at 1.31.57 PM_ 20251028044557PM-thumb.png END:VEVENT BEGIN:VEVENT RECURRENCE-ID;TZID=America/New_York:20251107T153000 CATEGORIES:Utilities CATEGORIES:Main CONTACT;X-BEDEWORK-UID=8a00ec8b-979413b9-0198-a4d89b28-00001675:Thompson\, Ekaterina CREATED:20250904T132242Z DESCRIPTION:In observational studies\, a single cause or exposure can infl uence multiple\, often highly correlated outcomes. For example\, in envir onmental health\, air pollution can simultaneously affect several disease s or causes of mortality\, while in genomics\, a treatment or cancer type can alter genomic or mutational signature profiles. Similarly\, wildfire smoke represents a specific exposure that can modify the chemical compos ition of pollutants in the air. We address this setting within the potent ial outcomes framework and propose a Bayesian causal regression factor mo del to estimate multivariate causal effects in the presence of correlated outcomes. Our approach introduces two key innovations:(i) a causal infer ence framework for multivariate potential outcomes\, and (ii) a novel Bay esian factor model that employs a dependent probit stick-breaking process as a distribution for treatment-specific factor scores. By modeling fact or scores directly\, the proposed method overcomes the missing data chall enges inherent in causal inference and flexibly captures latent structure s underlying outcome correlations. We apply our method to U.S. air qualit y data\, estimating the causal effect of wildfire smoke on 27 chemical sp ecies in fine particulate matter pm2.5\, providing a deeper understanding of their interdependencies. DURATION:PT1H DTSTAMP:20251103T131718Z DTSTART;TZID=America/New_York:20251107T153000 LAST-MODIFIED:20251103T131718Z LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi stry 116 STATUS:CONFIRMED SUMMARY:Multivariate Causal Effect: a Bayesian Regression Factor Model UID:CAL-8a00ec8b-979413b9-0199-14e4ca9c-0000776cdemobedework@mysite.edu X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-SPEAKER:Dafne Zorzetto X-BEDEWORK-DUKE-SERIES:Friday Seminar in the department of Statistical Sci ence X-BEDEWORK-SUBMITTEDBY:ek217 for Statistical Science (agrp_StatisticalScie nce) X-BEDEWORK-IMAGE-X1:201 X-BEDEWORK-IMAGE-Y1:58 X-BEDEWORK-IMAGE-X2:1877 X-BEDEWORK-IMAGE-Y2:1175.3333333333333 X-BEDEWORK-IMAGE-CROP-WIDTH:1676 X-BEDEWORK-IMAGE-CROP-HEIGHT:1117.3333333333333 X-BEDEWORK-IMAGE-ALT-TEXT:Dafne Zorzetto X-BEDEWORK-IMAGE:/public/Images/headshot - Dafne_20251103011718PM.jpg X-BEDEWORK-THUMB-IMAGE:/public/Images/headshot - Dafne_20251103011718PM-th umb.png END:VEVENT BEGIN:VEVENT RECURRENCE-ID;TZID=America/New_York:20251114T153000 CATEGORIES:Utilities CATEGORIES:Main CONTACT;X-BEDEWORK-UID=8a00ec8b-979413b9-0198-a4d89b28-00001675:Thompson\, Ekaterina CREATED:20250904T132242Z DESCRIPTION:Over the past decade\, researchers have made significant progr ess in understanding when structured systems for data recovery achieve th eir theoretical limits. For error-correcting codes\, which enable reliabl e communication despite data loss or noise\, some symmetric code families (such as Reed-Muller and BCH codes) are now known to achieve optimal thr oughput on erasure channels. Compressed sensing\, where the goal is to re construct a high-dimensional sparse signal from a small number of linear measurements\, is known to have a strong mathematical connection to error -correcting codes. In this talk\, I will explore how symmetry can provide strong performance guarantees for both problems. All results will be int roduced using linear algebra and probability without assuming knowledge o f coding theory or compressed sensing. DURATION:PT1H DTSTAMP:20251110T223611Z DTSTART;TZID=America/New_York:20251114T153000 LAST-MODIFIED:20251110T223611Z LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi stry 116 STATUS:CONFIRMED SUMMARY:The Power of Symmetry in Coding and Sensing UID:CAL-8a00ec8b-979413b9-0199-14e4ca9c-0000776cdemobedework@mysite.edu X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-SPEAKER:Henry Pfister X-BEDEWORK-DUKE-SERIES:Friday Seminar in the department of Statistical Sci ence X-BEDEWORK-SUBMITTEDBY:ek217 for Statistical Science (agrp_StatisticalScie nce) X-BEDEWORK-IMAGE-X1:5 X-BEDEWORK-IMAGE-Y1:45 X-BEDEWORK-IMAGE-X2:1418 X-BEDEWORK-IMAGE-Y2:987 X-BEDEWORK-IMAGE-CROP-WIDTH:1413 X-BEDEWORK-IMAGE-CROP-HEIGHT:942 X-BEDEWORK-IMAGE-ALT-TEXT:Henry Pfister X-BEDEWORK-IMAGE:/public/Images/Screenshot 2025-11-10 at 5.34.27 PM_202511 10103611PM.png X-BEDEWORK-THUMB-IMAGE:/public/Images/Screenshot 2025-11-10 at 5.34.27 PM_ 20251110103611PM-thumb.png END:VEVENT BEGIN:VEVENT RECURRENCE-ID;TZID=America/New_York:20251121T153000 CATEGORIES:Utilities CATEGORIES:Main CONTACT;X-BEDEWORK-UID=8a00ec8b-979413b9-0198-a4d89b28-00001675:Thompson\, Ekaterina CREATED:20250904T132242Z DESCRIPTION:This paper investigates the theoretical foundation and develop s analytical formulas for sample size and power calculations for causal i nference with observational data. By analysing the variance of the invers e probability weighting estimator of the average treatment effect\, we de compose the power calculations into three components: propensity score di stribution\, potential outcome distribution\, and their correlation. We s how that to determine the minimal sample size of an observational study\, it is sufficient under mild conditions to have two parameters additional to the standard inputs in the power calculation of randomised trials\, w hich quantify the strength of the confounder-treatment and the confounder -outcome association\, respectively. For the former\, we propose using th e Bhattacharyya coefficient\, which measures the covariate overlap and\, together with the treatment proportion\, leads to a uniquely identifiable and easily computable propensity score distribution. For the latter\, we propose a sensitivity parameter bounded by the R-squared statistic of th e regression of the outcome on covariates. Utilising the Lyapunov Central Limit Theorem on the linear combination of covariates\, our procedure do es not require distributional assumptions on the multivariate covariates. This is a joint work with Bo Liu. DURATION:PT1H DTSTAMP:20251119T160701Z DTSTART;TZID=America/New_York:20251121T153000 LAST-MODIFIED:20251119T160701Z LOCATION;X-BEDEWORK-UID=18832edc-1b27e154-011b-28365daf-0000006c:Old Chemi stry 116 STATUS:CONFIRMED SUMMARY:Sample size and power calculations for causal inference in observa tional studies UID:CAL-8a00ec8b-979413b9-0199-14e4ca9c-0000776cdemobedework@mysite.edu X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili ties/Main X-BEDEWORK-SPEAKER:Fan Li X-BEDEWORK-DUKE-SERIES:Friday Seminar in the department of Statistical Sci ence X-BEDEWORK-SUBMITTEDBY:ek217 for Statistical Science (agrp_StatisticalScie nce) X-BEDEWORK-IMAGE-X1:0 X-BEDEWORK-IMAGE-Y1:0 X-BEDEWORK-IMAGE-X2:4197 X-BEDEWORK-IMAGE-Y2:2798 X-BEDEWORK-IMAGE-CROP-WIDTH:4197 X-BEDEWORK-IMAGE-CROP-HEIGHT:2798 X-BEDEWORK-IMAGE-ALT-TEXT:Fan Li X-BEDEWORK-IMAGE:/public/Images/CEM_Fan Li-min_20251119040701PM.jpg X-BEDEWORK-THUMB-IMAGE:/public/Images/CEM_Fan Li-min_20251119040701PM-thum b.png END:VEVENT END:VCALENDAR