Monte Carlo Simulation Study for the EVBS Regression Model

Runs a Monte Carlo simulation that evaluates the finite-sample properties of the joint maximum likelihood estimator of the EVBS regression model under one of three scenarios.

Usage

evbsreg.fit.mc(
  m,
  n,
  beta0,
  beta1,
  alpha,
  gama,
  scenario = c("canonical", "leverage", "robustness"),
  semente = 2023
)

Arguments

m

Number of Monte Carlo replicates (the paper uses 5000; set to a smaller value such as 500 for a quick check).

n

Sample size for each replicate (the paper uses 60, 120, 180).

beta0

True intercept \(\beta_0\).

beta1

True slope \(\beta_1\).

alpha

True shape parameter \(\alpha\).

gama

True tail-shape parameter \(\gamma\).

scenario

Character string selecting the design:

"canonical"

covariate \(x \sim U(0,1)\), no contamination (baseline).

"leverage"

10% of covariate values drawn from \(U(5,10)\) to introduce high-leverage points.

"robustness"

10% of observations generated with the shape parameter shifted by \(-0.5\) (alpha contamination).

semente

Integer RNG seed for reproducibility (default 2023).

Value

A numeric matrix of dimension \(10 \times 4\). Columns are the four parameters Beta0, Beta1, Alpha, Gama; rows are: Parametro (true value), EMV (mean estimate), VIES-ABS (absolute bias), VIES-REL (relative bias), VAR (empirical variance), EQM (mean squared error), E-PADRAO (empirical standard error), EP-FISHER (mean Fisher standard error), RAIZ-EQM (root mean squared error), and TAXA-COB (empirical 95% coverage rate).

References

Ospina, R., Lima, J. I. C., Barros, M., and Macedo, A. M. S. (2026). Local influence diagnostics for the extreme-value Birnbaum-Saunders regression model. Submitted.

See also

revbs, evbsreg.fit.

Examples

# \donttest{
## Quick check with m = 50 replicates
res <- evbsreg.fit.mc(m = 50, n = 60,
                       beta0 = 0.5, beta1 = 0.5,
                       alpha = 0.5, gama = 0.20,
                       scenario = "canonical")
print(res)
#>            Beta0 Beta1  Alpha  Gama
#> Parametro  0.500 0.500  0.500 0.200
#> EMV        0.488 0.500  0.469 0.234
#> VIES-ABS  -0.012 0.000 -0.031 0.034
#> VIES-REL  -0.024 0.001 -0.061 0.170
#> VAR        0.022 0.046  0.004 0.022
#> EQM        0.022 0.046  0.005 0.023
#> E-PADRAO   0.148 0.213  0.066 0.149
#> EP-FISHER  0.125 0.197  0.057 0.121
#> RAIZ-EQM   0.148 0.213  0.073 0.153
#> TAXA-COB   0.860 0.980  0.860 0.880
# }