A Theory of Truncated Inverse Sampling
Statistics Theory
2013-11-05 v2 Machine Learning
Probability
Methodology
Statistics Theory
Abstract
In this paper, we have established a new framework of truncated inverse sampling for estimating mean values of non-negative random variables such as binomial, Poisson, hyper-geometrical, and bounded variables. We have derived explicit formulas and computational methods for designing sampling schemes to ensure prescribed levels of precision and confidence for point estimators. Moreover, we have developed interval estimation methods.
Cite
@article{arxiv.0810.5551,
title = {A Theory of Truncated Inverse Sampling},
author = {Xinjia Chen},
journal= {arXiv preprint arXiv:0810.5551},
year = {2013}
}
Comments
31 pages, no figure, revised proofs