High dimensional BIC for quantile regression model
Description
A high dimensional BIC will be returned specificall for quantile regression
Usage
1 
Arguments
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. 
Details
The high dimensional BIC for quantile regression model is
log(checkloss)+Slog(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
.
Value
QBIC will be returned, which is a numerical value
Author(s)
Bo Peng
References
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 \Sexpr[results=rd,stage=build]{tools:::Rd_expr_doi("10.1080/01621459.2013.836975")}
Wang,L., Kim, Y., and Li,R. (2013+) Calibrating nonconvex penalized regression in ultrahigh dimension. To appear in Annals of Statistics. http://users.stat.umn.edu/~wangx346/research/nonconvex.pdf
See Also
checkloss
, QICD
Examples
1 2 3 4 5 6 7 8 9 10 