Monte Carlo Size/Power Simulation for the Bootstrap U_n Test

Replicates the Monte Carlo study of Espinheira et al. (2026), computing empirical rejection rates of the bootstrap \(U_n\) test under correct specification (size) or misspecification (power).

Usage

sim_table1(
  n = 40,
  beta = c(-3, 2, 1, -1, 0.5),
  sigma2 = 0.5,
  R = 5000,
  B = 1000,
  alpha = c(0.01, 0.05, 0.1),
  mu_range = c("low", "mid", "high"),
  ncores = 1,
  seed = NULL
)

Arguments

n: Sample size.
beta: Numeric vector of mean-model coefficients.
sigma2: Dispersion parameter (constant model).
R: Number of Monte Carlo replications.
B: Number of bootstrap replicates per replication.
alpha: Significance levels.
mu_range: One of "low", "mid", "high"; used to select the covariate configuration that places fitted means near 0, near 0.5, or near 1.
ncores: Number of parallel workers for the outer MC loop. Default 1.
seed: Random seed. Default NULL.

Value

A data frame with columns alpha and rej_rate.

Examples

# \donttest{
res <- sim_table1(n = 40, beta = c(-3, 2, 1, -1, 0.5),
                  sigma2 = 0.5, R = 100, B = 100,
                  mu_range = "mid", ncores = 1)
print(res)
#>   alpha rej_rate  n sigma2 mu_range
#> 1    1%        4 40    0.5      mid
#> 2    5%        7 40    0.5      mid
#> 3   10%       12 40    0.5      mid
# }