Description Usage Arguments Details Value Author(s) References See Also Examples
Use the high dimensional BIC for quantile regression model on QICD
algorithm, produces a plot and return a value for lambda
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y |
response |
x |
|
beta |
|
const |
a parameter to adjust the high dimensional BIC. A positive numerical value. |
tau |
|
lambda |
a user supplied |
a |
|
funname |
|
intercept |
|
thresh |
|
maxin |
|
maxout |
|
plot.off |
a logical value to control if a plot of QBIC vs. |
... |
other argument that can be passed to |
The function run QICD
nfolds
times. For each specific lambda
, the QBIC will be produced for comparison. Claim that cv.QICD
does NOT search for values of a
.
an object of class "BIC.QICD" is returned, which is a list with the components of QBIC.
lambda |
the values of |
HBIC |
The high dimensional BIC is given-vector of length nlambda, as in |
nzero |
number of non-zero coefficients at each |
lambda.min |
value of |
Bo Peng
Peng,B and Wang,L. (2015)An Iterative Coordinate Descent Algorithm for High-dimensional Nonconvex Penalized Quantile Regression, Journal of Computational and Graphical Statistics http://amstat.tandfonline.com/doi/abs/10.1080/10618600.2014.913516 doi: 10.1080/10618600.2014.913516
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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