Bias Reduction of Estimates By Bootstrap Method

In general it is desirable to have unbiased estimators for parameters of a probability distribution function. However, there are several estimators which are not unbiased. In this thesis, we show by direct computation that the bias of the bootstrap estimate of μ⁴ can be reduced, where μ is the mean of the population. We consider both nonparametric and parametric cases. In the parametric case, the bias of the bootstrap estimate is computed for normal, exponential and Poisson distributions.

Name thesis:degree_name

Master of Science in Mathematics

Level thesis:degree_level

Masters

Discipline thesis:degree_discipline

Mathematics

Year

1995

Author dc:creator

• Schuchman, Linda J.

Contributors dc:contributor

• George Mathew

• Mathematics

Statement dc:rights

• © Linda J. Schuchman

Repository record dc:identifier

https://bearworks.missouristate.edu/theses/1021

OAI identifier oai:identifier

oai:bearworks.missouristate.edu:theses-2022