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seminarios:resumos [2007/03/06 10:52] ehlers |
seminarios:resumos [2007/03/08 20:28] ehlers |
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== Resumos == | == Resumos == | ||
- | *Accounting for Model Uncertainty via Trans-dimensional Genetic Algorithms | + | |
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+ | Apresentar-se-á noções teóricas de algumas das mais freqüentemente utilizadas análises de dados categorizados completos | ||
+ | ou com omissão (i.e., que possuem dados incompletos ou faltantes) e ilustrar-se-á como realizar tais análises com a ajuda | ||
+ | da biblioteca de rotinas Catdata implementada para o ambiente estatístico R. | ||
+ | |||
+ | *Accounting for Model Uncertainty via Trans-dimensional Genetic Algorithms (Ricardo Ehlers) | ||
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. | 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. |