Bootstrap confidence intervals for model based surveys Academic Article uri icon

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.

publication date

  • 2005

keywords

  • Bootstrap
  • Confidence Interval
  • Model-based approach.

number of pages

  • 6

start page

  • 84

end page

  • 90

volume

  • 1

issue

  • 1