reported by:

i\. The average number of the true zero coefficients of $b_0$ that are correctly set to zero, denoted by $C(b_0)$.

ii\. The average number of the true non-zero coefficients of $b_0$ that are incorrectly set to zero, which is given by $IC(b_0)$.

iii\. The proportion of samples that excluding any non-zero coefficients, denoted by $U-$fit.

iv\. The proportion of samples selecting the exact subset models (correct-fit) and the proportion of smaples including all the variables (over-fit), labeled by $C-$fit and $O-$fit respectively.

From Table \[table: non-zero param\], it is clear that for both of the two cases $N=400$ and $N=800$, the fitting to the non-zero coefficients are very good while the fitting accuracy in the case of $N=800$ is even better. As for variable selection, Table \[table: variable selection\] shows that in most of the samples, the set of zero variables can be exactly identified by our procedure. In particular, as the sample size increases, the identification accuracy is risen up as well. Even in the rare samples where some zero variables are misclassified, the misclassification happened sparsely as $C(b_0)>12$ and that value is close to the true value, $13$.

Real Example {#real example}
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The New York 