Sala de seminários do Departamento de Estatística- UFSCar-São
Carlos-SP
Títuo: Exploring Large Regression Model Spaces via
Trans-dimensional Genetic Algorithms
Ricardo Ehlers (ICMC-USP)
Abstract
We develop for regression models trans-dimensional genetic
algorithms
for the exploration of large model spaces. Our algorithms can be
used
in two different ways. The first possibility is to search the
best
model according to some criteria such as AIC or BIC. The
second
possibility is to use our algorithms to explore the model
space,
search for the most probable models and estimate their
posterior
probabilities. This is accomplished by the use of genetic
operators
embedded in a reversible jump Markov chain
Monte Carlo algorithm
in the model space with several chains.
As these chains run simultaneously
and learn from each other via the
genetic operators, our algorithm
efficiently explores the large model
space and easily escapes local maxima
regions common in the presence of
highly correlated regressors. We illustrate
the power of our
trans-dimensional genetic algorithms with applications to
two real
data sets.
Key Words: Model comparison, Genetic algorithms, Markov chain
Monte
Carlo, reversible jump MCMC.
Estão todos convidados para este seminário especial às
14:00 h. Lembramos que neste semestre o GIB realizará mensalmente um
seminário conjunto com o ICMC-USP. Abraços,
Josemar