INTERVAL ESTIMATION OF DISTRIBUTION PARAMETER BY STATISTICAL TRIALS OF EXPECTED VALUE

The distribution parameter interval estimators are obtained by direct numerical approximation of the expected value for infinite and finite populations under the known upper and lower bounds of the random variable domain. Like in Bayesian approach, the distribution parameters are treated as random variables, and their uncertainty is described as a distribution. The Monte Carlo procedure is involved to get the correspondent confidence interval endpoints. The model does not impose any restrictions on the type of distributions. In contrast to other nonparametric interval assessments of distribution parameters, the model is operable for samples of any size.