Bayesian Variable Selection for High Dimensional Generalized Linear Models: Convergence Rates of the Fitted Densities by Wenxin Jiang

@inproceedings{Jiang2006BayesianVS,
  title={Bayesian Variable Selection for High Dimensional Generalized Linear Models: Convergence Rates of the Fitted Densities by Wenxin Jiang},
  author={Wenxin Jiang},
  year={2006}
}
Bayesian variable selection has gained much empirical success recently in a variety of applications when the number K of explanatory variables (x1, . . . , xK) is possibly much larger than the sample size n. For generalized linear models, if most of the xj ’s have very small effects on the response y, we show that it is possible to use Bayesian variable selection to reduce overfitting caused by the curse of dimensionality K n. In this approach a suitable prior can be used to choose a few out of… CONTINUE READING
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