- Parametric bootstrap closely related to objective Bayes. (That’s why it’s a good importance sampling choice.)
- When it applies, parboot approach has both computational and interpretational advantages over MCMC/Gibbs. (...)
- (...) the use of parametric bootstrap sampling to carry out Bayesian inference calculations. (...) possible in a subset of those problems amenable to MCMC analysis, but when feasible the bootstrap approach offers both computational and theoretical advantages."
set.seed(12345) n <- 20000 dat <- data.frame(x = rnorm(n)) dat$y <- ((2 + 2 * dat$x + + rlogis(n)) > 0) + 0 library(margins) fit.logit <- glm(y ~ x, binomial, dat) summary(margins(fit.logit, variables = "x"))
My questions are:
1) Are the SE from the
delta method more "robust" than those that would be obtained via the
2) If not, how would the
parboot approach be implemented to estimate SE for ME in R?
Any help would be much appreciated.