Open AccessINTERVAL ESTIMATION OF DISTRIBUTION PARAMETER BY STATISTICAL TRIALS OF EXPECTED VALUE
Author(s) -
Valeriy Barannik
Publication year - 2019
Publication title -
problems of atomic science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.216
H-Index - 17
eISSN - 1562-6016
pISSN - 1682-9344
DOI - 10.46813/2019-124-138
Subject(s) - estimator , interval estimation , mathematics , confidence interval , random variable , credible interval , statistics , monte carlo method , interval (graph theory) , nonparametric statistics , distribution (mathematics) , coverage probability , mathematical analysis , combinatorics
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.