[ABE-L] Convite: Seminário DEST/UFMG em 18/06/2021.

[Vinicius Mayrink]

Caros,

Na próxima sexta-feira (*18 de junho, às 13:30h*) o ciclo de
Seminários do *Departamento
de Estatística da UFMG* terá a apresentação de *Eduardo Fonseca Mendes*.

Eduardo é professor da EMAp - FGV (Rio de Janeiro). Ele desenvolve pesquisa
em Machine Learning, Métodos de simulação Monte Carlo e Econometria teórica.

O seminário será transmitido ao vivo pelo canal do Youtube *"Seminários
DEST - UFMG <https://www.youtube.com/channel/UCoZC2_pME9ca_-Hx4djd60w>".*

Att,
Vinícius Mayrink


*********** Título e Resumo ***********
Eduardo Fonseca Mendes (EMAp - FGV, Rio de Janeiro).

*Sparsity dependent generalized information criteria for regularized
m-estimadors. *

Regularized M-estimators are widely used due to their ability to recover a
low-dimensional model in high-dimensional scenarios. Some recent efforts on
this subject focused on creating a unified framework for establishing
oracle bounds, and deriving conditions for support recovery. Under this
same framework, we propose a new Generalized Information Criteria that
takes into consideration the sparsity pattern one wishes to recover. We
obtain sufficient conditions for model selection consistency of the GIC and
path consistency of regularized m-estimators. In other words, we show that
under conditions on the penalty function, one may use the GIC for selecting
the regularization parameter in a way that the sequence of model subspaces
contains the true model with probability converging to one. This allows
practical use of the GIC for model selection in high-dimensional scenarios.
We illustrate those conditions on examples including LASSO regression and
group sparse generalized linear regression.

-- 
*Vinícius D. Mayrink*
*Professor Associado - Departamento de Estatística*

*ICEx, Universidade Federal de Minas Gerais*
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