[ABE-L] Webinar em High Dimensional Data Analysis

[Ronaldo Dias]

THE LAST BUT NOT THE LEAST.

Prezados(as)
último seminário deste ano 2020, inesquecível por diversas razões. Como
sempre  ocorrerá às 13hs, *16/12/2020*. Os links para este seminário e a
gravação do anterior (google meet e youtube) estão abaixo. Desta vez
teremos como palestrante, *Prof. Rafael Izbicki, UFSCAR*. Os
interessados(as) sejam bem vindos.

*Webinar 16/12/2020*: https://meet.google.com/bzx-pbva-imx

Título: Confidence Sets and Hypothesis Testing in a Likelihood-Free
Inference Setting

Abstract: Parameter estimation, statistical tests and confidence sets are
the cornerstones of classical statistics that allow scientists to make
inferences about the underlying process that generated the observed data. A
key question is whether one can still construct hypothesis tests and
confidence sets with proper coverage and high power in a so-called
likelihood-free inference (LFI) setting, where the likelihood is not
explicitly known but one can forward-simulate observable data according to
a stochastic model. In this paper, we present ACORE (Approximate
Computation via Odds Ratio Estimation), a frequentist approach to LFI that
first formulates the classical likelihood ratio test (LRT) as a
parametrized classification problem, and then uses the equivalence of tests
and confidence sets to build confidence regions for parameters of interest.
We also present a goodness-of-fit test for checking whether the constructed
tests and confidence regions are valid. ACORE is based on the key
observation that the LRT statistic, the rejection probability of the test,
and the coverage of the confidence set are conditional distribution
functions which often vary smoothly as a function of the parameters of
interest. Hence, instead of relying solely on samples simulated at fixed
parameter settings (as is the convention in standard Monte Carlo
solutions), one can leverage machine learning tools and data simulated in
the neighborhood of a parameter to improve estimates of quantities of
interest. We demonstrate the efficacy of ACORE with both theoretical and
empirical results.

webinar 9/12/2020: https://youtu.be/l8sM9HCxGoE


-- 
Ronaldo Dias, Ph.D.
Professor
Dept. of Statistics-IMECC, UNICAMP
www.ime.unicamp.br/~dias
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