Description Usage Arguments Details Value Author(s) References See Also Examples
A high dimensional BIC will be returned specificall for quantile regression
1 |
y |
response |
X |
|
beta |
the coefficients vector for BIC calculation |
tau |
|
const |
a constant to adjust the BIC. A positive numerical value; default value is 6. |
The high dimensional BIC for quantile regression model is
log(checkloss)+|S|log(log(n))C_n/n
where S is the selected model in QICD, n is the number of obs, C_n is some positive constant which diverges to infinity as n increases. Actually, C_n is log(p)/const
.
QBIC will be returned, which is a numerical value
Bo Peng
Lee, E. R., Noh, H. and Park. B. (2013) Model Selection via Bayesian Information Criterion for Quantile Regression Models. Journal of the American Statistical Associa- tion, preprint. http://www.tandfonline.com/doi/pdf/10.1080/01621459.2013.836975 doi: 10.1080/01621459.2013.836975
Wang,L., Kim, Y., and Li,R. (2013+) Calibrating non-convex penalized regression in ultra-high dimension. To appear in Annals of Statistics. http://users.stat.umn.edu/~wangx346/research/nonconvex.pdf
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