| getNPPIblksizesQR {QregBB} | R Documentation |
Chooses block sizes for MBB, ETBB, SMBB, and SETBB via the NPPI for quantile regression
Description
Chooses block sizes for MBB, ETBB, SMBB, and SETBB via the NPPI for quantile regression
Usage
getNPPIblksizesQR(Y, X, tau, min.in.JAB = 100)
Arguments
Y |
the vector of response values. |
X |
the design matrix (including a column of ones for the intercept). |
tau |
the quantile of interest. |
min.in.JAB |
the minimum number of Monte-Carlos draws desired in each jackknife draw |
Details
This function is based on the nonparametric plug-in (NPPI) method discussed in Lahiri (2003), which makes use of the jackknife-after-bootstrap (JAB).
Value
Returns a list of the NPPI-selected block sizes for the MBB, SMBB, ETBB, and SETBB.
References
Gregory, K. B., Lahiri, S. N., & Nordman, D. J. (2018). A smooth block bootstrap for quantile regression with time series. The Annals of Statistics, 46(3), 1138-1166.
Lahiri, S. N. (2003). Resampling Methods for Dependent Data. Springer, New York.
Examples
# generate some data and use NPPI to choose block sizes for MBB, SMBB, ETBB, and SETBB.
n <- 50
X1 <- arima.sim(model=list(ar=c(.7,.1)),n)
X2 <- arima.sim(model=list(ar=c(.2,.1)),n)
e <- arima.sim(model=list(ar=c(.7,.1)),n)
Y <- X1 + e
X <- cbind(rep(1,n),X1,X2)
blksize.out <- getNPPIblksizesQR(Y,X,tau=.5)
blksize.out