TRAINING KEY 162: Bootstrap Technique

Practical Methods for Design and Analysis of Complex Surveys.
Risto Lehtonen and Erkki Pahkinen

TRAINING KEY 162: Bootstrap Technique


Example 5.4. Similar to the other sample re-use methods and the linearization method, the bootstrap can be used for variance approximation of a nonlinear estimator under a complex sampling design. In this Training Key we apply the BOOT technique for variance approximation of a subpopulation proportion estimator for the binary response variable CHRON (chronic morbidity) and a subpopulation mean estimator for the continuous response variable SYSBP (systolic blood pressure) in the MFH survey. The subgroup considered is males aged 30-64 years.


In this part we will demonstrate how the estimation of the variance of a combined ratio type proportion or mean estimator can be carried out with the bootstrap method. We will also demonstrate how the number of bootstrap samples generated affects to the distribution of bootstrap estimates. The reason for the use of an approximative variance estimator is that the proportion and mean estimators should be treated here as nonlinear estimators of type y/x where both the numerator y and the denominator x are random variables. The denominator x is random because the sampling design of the MFH Survey is a stratified two-stage design where the cluster sample sizes are not fixed in advance, and the subgroup size is not fixed.



Please download the SAS macro BOOT for your own further bootstrap method training. Instructions will be given in the code once you download it.

NOTE! You need to have access to SAS in your computer.