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DTSTAMP:20260916T102711
DTSTART;TZID=America/Detroit:20261106T100000
DTEND;TZID=America/Detroit:20261106T110000
SUMMARY:Social / Informal Gathering:La Tertulia: Spanish Coffee Hour
DESCRIPTION:Hola! ¿Cómo estás?\n\n-Practice your Spanish-speaking skills with peers & instructors in a relaxed environment. All language levels and students are welcome to join the conversation.\n\n-Come & go as you please\; stay as little or as long as you would like!\n\n-Free coffee\, tea\, light snacks\, & baked goods.
UID:150691-21909925@events.umich.edu
URL:https://events.umich.edu/event/150691
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Coffee,Undergraduate Students,Undergraduate,Study Abroad,Spanish,Spain,Social,Romance Languages And Literatures,Networking,Latin America,Language,Games,Central America,Community,Community Engagement,Culture,Engaged Learning,Europe,European,Food,Free,International
LOCATION:Modern Languages Building - 4314 MLB
CONTACT:
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20261029T111003
DTSTART;TZID=America/Detroit:20261106T100000
DTEND;TZID=America/Detroit:20261106T110000
SUMMARY:Workshop / Seminar:Neyman-Orthogonal Tests for Equality of Regression Functions
DESCRIPTION:We develop a test for equality of regression functions across two populations that allows unequal covariate distributions and uses flexible modern learners for regression estimation. We target the regression contrast through pseudo-residuals\, which we construct by multiplying residuals from the pooled outcome regression by the difference between the population indicator and its propensity. Under overlap\, the conditional mean of the proposed pseudo-residuals identifies the regression contrast. Furthermore\, the pseudo-residuals satisfy Neyman orthogonality\, which removes the first-order population bias from nuisance estimation and allows us to test the regression contrast under weaker nuisance estimation rate requirements. To construct our test statistic\, we embed the conditional moment of the pseudo-residuals in a reproducing kernel Hilbert space (RKHS)\, leading to a simple closed form expression and calibrate its empirical norm using a Gaussian multiplier bootstrap. We establish a finite-sample calibration bound that separates oracle Gaussian approximation\, nuisance estimation\, and covariance estimation errors. Our main result gives conditions under which we can use the same observations for both nuisance fitting and testing\, yielding valid and powerful inference without sample splitting. In a fixed Gaussian regression model\, we establish matching lower and upper bounds for the minimax separation order (n^{-1/2}) in the RKHS discrepancy and show that the proposed test attains this order. We conduct extensive numerical studies to examine size and power across learners and demonstrate power gains over a single sample split and other competitors and illustrate the test on an exploratory application to negotiation dialogues for regression comparison with text covariates.
UID:153811-21916382@events.umich.edu
URL:https://events.umich.edu/event/153811
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Aem Featured,seminar,statistics,Talk
LOCATION:West Hall - 340
CONTACT:
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