Bootstrap confidence intervals for model-based surveys
Abstract
A main shortcoming of the conventional method of constructing a confidence interval for a finite population parameter e.g. the mean/ total is that it assumes that the sample size is large enough for the central limit theorem to apply to the estimation error. This is not always the case in practice. To deal with the problem, Chambers and Dorfam (1994) suggested a n alternative method based on the bootstrap methodology. Their method is meant for model-based surveys. It starts by assuming a simple linear regression model as a working model in which the ratio estimator is optimal for estimating the population total. To achieve robustness in their results, a series of modifications is carried out on the ratio estimator. This makes their method cumbersome to apply. In this paper we suggest an alternative bootstrap approach that is simpler to implement.
Keywords: Bootstrap; Confidence Interval; Model-based approach.
> East African Journal of Statistics Vol. 1 (1) 2005: pp. 84-90
Published
2007-07-17
Issue
Section
Articles
Submission of papers to East African Journal of Statistics will be taken to imply that it presents original work, not under consideration for publication elsewhere. By submitting a manuscript authors agree that the copyright for their article is transferred to the publisher if and when the article is accepted for publication. The copyright covers the exclusive right to reproduce and distribute the article, including reprints, photographic reproductions, microfilm, or any other reproduction of similar or any nature including translations.