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DTSTART;TZID=Europe/Helsinki:20200113T123000
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DTSTAMP:20260814T155059
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SUMMARY:Elia LAPENTA (TSE) "A Bootstrap Specification Test for Semiparametric Models with Generated Regressors"
DESCRIPTION:Job market interview\nTime: 12:30pm – 13:45 pm\nDate: 13th of January 2020\nPlace: Room 3001\nElia LAPENTA (TSE) “A Bootstrap Specification Test for Semiparametric Models with Generated Regressors”\nAbstract : This paper provides a specification test for semiparametric models with nonparametrically generated regressors. Such variables are not observed by the researcher but are nonparametrically identified and estimable. Applications of the test include models with endogenous regressors identified by control functions\, semiparametric sample selection models\, or binary games with incomplete information. The statistic is built from the residuals of the semiparametric model\, and a novel wild bootstrap procedure is shown to provide valid critical values. We consider nonparametric estimators with an automatic bias correction that makes the test implementable without undersmoothing. In simulations the test exhibits good small-sample performances\, and an application to women’s labor force participation decisions shows the implementation of the test in a real-data context.\n  \n
URL:https://crest.science/event/elia-lapenta-2/
CATEGORIES:Economics
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DTEND;TZID=Europe/Paris:20200113T151500
DTSTAMP:20260814T155059
CREATED:20200107T134311Z
LAST-MODIFIED:20200107T134311Z
UID:12396-1578924000-1578928500@crest.science
SUMMARY:Marco AVELLA (Columbia University) - " Privacy-preserving parametric inference: A case for robust statistics "
DESCRIPTION:\nThe Statistical Seminar: Every Monday at 2:00 pm.\nTime: 2:00 pm – 3:15 pm\nDate: 13th of January 2020\nPlace: Room 3001.\nMarco AVELLA (Columbia University) – ” Privacy-preserving parametric inference: A case for robust statistics “ \nAbstract: Differential privacy is a cryptographically-motivated approach to privacy that has become a very active field of research over the last decade in theoretical computer science and machine learning. In this paradigm one assumes there is a trusted curator who holds the data of individuals in a database and the goal of privacy is to simultaneously protect individual data while allowing the release of global characteristics of the database. In this setting we introduce a general framework for parametric inference with differential privacy guarantees. We first obtain differentially private estimators based on bounded influence M-estimators by leveraging their gross-error sensitivity in the calibration of a noise term added to them in order to ensure privacy. We then show how a similar construction can also be applied to construct differentially private test statistics analogous to the Wald\, score and likelihood ratio tests. We provide statistical guarantees for all our proposals via an asymptotic analysis. An interesting consequence of our results is to further clarify the connection between differential privacy and robust statistics. In particular\, we demonstrate that differential privacy is a weaker stability requirement than infinitesimal robustness\, and show that robust M-estimators can be easily randomized in order to guarantee both differential privacy and robustness towards the presence of contaminated data. We illustrate our results both on simulated and real data. \n \nOrganizers:\nCristina BUTUCEA\, Alexandre TSYBAKOV\, Julie JOSSE\, Eric MOULINES\, Mathieu ROSENBAUM\nSponsors:\nCREST-CMAP\n \n\n
URL:https://crest.science/event/marco-abella/
CATEGORIES:Statistics
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