BSDesparseLasso: Bootstrap-calibrated Desparsified Lasso

Description Usage Arguments Value

View source: R/BSDesparseLasso.R

Description

This method first constructs the debiased estimator of β via the desparsified Lasso procedure. Then it calculates the calibration term \hat{b}_{max} =(1-n^{r-0.5})(\hat{β}_{max}-\hat{β}_{j,lasso}). Through B bootstrap iterations, it recalibrates the bootstrap statistic T_b. The bias-reduced estimate is computed as: \hat{b}_{max}-\frac{1}{B}∑_{b=1}^BT_b.

Usage

1
BSDesparseLasso(y, x, r = NULL, G = NULL, B = NULL, alpha = 0.95, fold = 3)

Arguments

y

response

x

design matrix

r

tuning parameter

G

subgroup indicator

B

bootstrap iterations

alpha

level of CI

Value

LowerBound

lower confidence bound

UpperBound

upper confidence bound

betaMax

bias-reduced maximum beta estimate

betaEst

debiased beta estimate for each subgroup

op

optimal tuning


WaverlyWei/debiased.subgroup documentation built on Jan. 27, 2021, 12:15 a.m.