l_adpt: Worst-case Excess Length of the Adaptive Procedure

In koohyun-kwon/rdadapt:

Worst-case Excess Length of the Adaptive Procedure

Description

Calculates E[U_min] in Proposition 4.4 of the paper (Ver20201104).

Usage

l_adpt(
  Cpr,
  Cvec,
  C,
  Xt,
  Xc,
  mon_ind,
  sigma_t,
  sigma_c,
  alpha,
  n_sim = 10^5,
  tau_res,
  bmat
)

Arguments

Cpr	the Lipschitz coefficient where the distance is evaluated at.
Cvec	a sequence of smoothness parameters
C	the Lipschitz coefficient for the function space we consider.
Xt	n_t by k design matrix for the treated units.
Xc	n_c by k design matrix for the control units.
mon_ind	index number for monotone variables.
sigma_t	standard deviation of the error term for the treated units (either length 1 or n_t).
sigma_c	standard deviation of the error term for the control units (either length 1 or n_c).
alpha	desired upper quantile value.
n_sim	number of simulated observations to calculate the expectation of the minimum of multivariate normal random variables; the default is n_sim = 10^5.
tau_res	a list produced by the function tau_calc; can be left unspecified.
bmat	a matrix of modulus values to be used in the adaptive procedure; can be left unspecified.

Examples

n <- 500
d <- 2
X <- matrix(rnorm(n * d), nrow = n, ncol = d)
tind <- X[, 1] < 0 & X[, 2] < 0
Xt <- X[tind == 1, ,drop = FALSE]
Xc <- X[tind == 0, ,drop = FALSE]
mon_ind <- c(1, 2)
sigma <- rnorm(n)^2 + 1
sigma_t <- sigma[tind == 1]
sigma_c <- sigma[tind == 0]
l_adpt(1/4, (1:5)/5, 2, Xt, Xc, mon_ind, sigma_t, sigma_c, 0.05)

koohyun-kwon/rdadapt documentation built on May 8, 2022, 8:49 p.m.