[ABE-L] Convite: Seminário DEST/UFMG (05/03 às 13:30hs).

[Vinicius Mayrink]

Caros,

Na próxima sexta-feira (*05 de março*) o ciclo de Seminários do *Departamento
de Estatística da UFMG* terá duas apresentações: Márcio A. F. Rodrigues (*às
13:30hs*) e Larissa N. A. Martins (*às 14:30hs*).

Márcio e Larissa são alunos do Programa de Doutorado em Estatística da UFMG.

Os seminários serão transmitidos ao vivo (a partir de 13:30hs) pelo canal
do Youtube "*Vídeo Conferência do DEST*":

https://www.youtube.com/channel/UCoZC2_pME9ca_-Hx4djd60w

Att,
Vinícius


*********** Seminário 1 às 13:30hs ***********

Márcio Augusto Ferreira Rodrigues (Doutorando em Estatística, DEST/UFMG)
Orientador: Enrico A. Colosimo.

*Semiparametric regression analysis of interval-censored competing risks
data.*
*Lu Mao, Dan-Yu Lin and Donglin Zeng*.

Interval-censored competing risks data arise when each study subject may
experience an event or failure from one of several causes and the failure
time is not observed directly but rather is known to lie in an interval
between two examinations. We formulate the effects of possibly time-varying
(external) covariates on the cumulative incidence or sub-distribution
function of competing risks (i.e., the marginal probability of failure from
a specific cause) through a broad class of semiparametric regression models
that captures both proportional and non-proportional hazards structures for
the sub-distribution. We allow each subject to have an arbitrary number of
examinations and accommodate missing information on the cause of failure.
We consider nonparametric maximum likelihood estimation and devise a fast
and stable EM-type algorithm for its computation. We then establish the
consistency, asymptotic normality, and semiparametric efficiency of the
resulting estimators for the regression parameters by appealing to modern
empirical process theory. In addition, we show through extensive simulation
studies that the proposed methods perform well in realistic situations.
Finally, we provide an application to a study on HIV-1 infection with
different viral subtypes.


*********** Seminário 2 às 14:30hs ***********

Larissa Natany Almeida Martins (Doutoranda em Estatística, DEST/UFMG)
Orientadores: Flávio B. Gonçalves e Thaís Paiva.

*A Bayesian network approach for population synthesis.*
*Lijun Sun and Alexander Erath.*

Agent-based micro-simulation models require a complete list of agents with
detailed demographic/socioeconomic information for the purpose of behavior
modeling and simulation. This paper introduces a new alternative for
population synthesis based on Bayesian networks. A Bayesian network is a
graphical representation of a joint probability distribution, encoding
probabilistic relationships among a set of variables in an efficient way.
Similar to the previously developed probabilistic approach, in this paper,
we consider the population synthesis problem to be the inference of a joint
probability distribution. In this sense, the Bayesian network model becomes
an efficient tool that allows us to compactly represent/reproduce the
structure of the population system and preserve privacy and confidentiality
in the meanwhile. We demonstrate and assess the performance of this
approach in generating synthetic population for Singapore, by using the
Household Interview Travel Survey (HITS) data as the known test population.
Our results show that the introduced Bayesian network approach is powerful
in characterizing the underlying joint distribution, and meanwhile the
overfitting of data can be avoided as much as possible.

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

*ICEx, Universidade Federal de Minas Gerais*
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