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Nonparametric kernel estimation of the probability density function of regression errors using estimated residuals
Kernel density estimation Leave-one-out kernel estimator Two-steps estimator
2010/10/14
This paper deals with the nonparametric density estimation of the regression error term assuming its independence with the covariate. The difference between the feasible estimator which uses the estim...
Regularization in regression: comparing Bayesian and frequentist methods in a poorly informative situation
Regularization regression comparing Bayesian frequentist methods
2010/10/14
We propose a global noninformative approach for Bayesian variable selection that builds on Zellner's g-priors and is similar to Liang et al. (2008). Our proposal does not require any kind of calibrati...