Consider some simulated data > set.seed(1) > x=exp(rnorm(100)) Assume that those data are observed i.id. random variables with distribution, with . The natural idea is to consider the maximum likelihood estimator For instance, consider some maximum likelihood estimator, > library(MASS) > (F=fitdistr(x,"gamma")) shape rate 1.4214497 0.8619969 (0.1822570) (0.1320717) > F$estimate+c(-1,1)*1.96*F$sd 1.064226 1.778673 Here, we have an approximated (since the...