[ABE-L] Seminário DEST/UFMG em 19/05/2023

[Marcos Prates]

Caros,

Na próxima sexta-feira (19 de Maio, às 13:30h) o ciclo de Seminários do
Departamento de Estatística da UFMG terá a apresentação do profa. Tamara
Broderick do MIT.

Tamara Broderick is an Associate Professor in the Department of Electrical
Engineering and Computer Science at MIT. She is a member of the MIT
Laboratory for Information and Decision Systems (LIDS), the MIT Statistics
and Data Science Center, and the Institute for Data, Systems, and Society
(IDSS). She completed her Ph.D. in Statistics at the University of
California, Berkeley in 2014. Previously, she received an AB in Mathematics
from Princeton University (2007), a Master of Advanced Study for completion
of Part III of the Mathematical Tripos from the University of Cambridge
(2008), an MPhil by research in Physics from the University of Cambridge
(2009), and an MS in Computer Science from the University of California,
Berkeley (2013). Her recent research has focused on developing and
analyzing models for scalable Bayesian machine learning. She has been
awarded selection to the COPSS Leadership Academy (2021), an Early Career
Grant (ECG) from the Office of Naval Research (2020), an AISTATS Notable
Paper Award (2019), an NSF CAREER Award (2018), a Sloan Research Fellowship
(2018), an Army Research Office Young Investigator Program (YIP) award
(2017), Google Faculty Research Awards, an Amazon Research Award, the ISBA
Lifetime Members Junior Researcher Award, the Savage Award (for an
outstanding doctoral dissertation in Bayesian theory and methods), the
Evelyn Fix Memorial Medal and Citation (for the Ph.D. student on the
Berkeley campus showing the greatest promise in statistical research), the
Berkeley Fellowship, an NSF Graduate Research Fellowship, a Marshall
Scholarship, and the Phi Beta Kappa Prize (for the graduating Princeton
senior with the highest academic average).

Título: An Automatic Finite-Sample Robustness Check: Can Dropping a Little
Data Change Conclusions?

Resumo: Practitioners will often analyze a data sample with the goal of
applying any conclusions to a new population. For instance, if economists
conclude microcredit is effective at alleviating poverty based on observed
data, policymakers might decide to distribute microcredit in other
locations or future years. Typically, the original data is not a perfect
random sample from the population where policy is applied -- but
researchers might feel comfortable generalizing anyway so long as
deviations from random sampling are small, and the corresponding impact on
conclusions is small as well. Conversely, researchers might worry if a very
small proportion of the data sample was instrumental to the original
conclusion. So we propose a method to assess the sensitivity of statistical
conclusions to the removal of a very small fraction of the data set.
Manually checking all small data subsets is computationally infeasible, so
we propose an approximation based on the classical influence function. Our
method is automatically computable for common estimators. We provide
finite-sample error bounds on approximation performance and a low-cost
exact lower bound on sensitivity. We find that sensitivity is driven by a
signal-to-noise ratio in the inference problem, does not disappear
asymptotically, and is not decided by misspecification. Empirically we find
that many data analyses are robust, but the conclusions of several
influential economics papers can be changed by removing (much) less than 1%
of the data.

O seminário será transmitido ao vivo pelo canal do Youtube "Seminários DEST
- UFMG <https://www.youtube.com/@seminariosdest-ufmg>".

https://www.youtube.com/@seminariosdest-ufmg

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