A stratified estimation for sensitive variable using correlated scrambling variables

Abstract

In this article, when the population is composed of several strata, we deal with the problem of stratified estimation for sensitive variables by applying stratified sampling to Murtaza et al.’s model using correlated scrambling variables. When the size of each stratum is accurately known, the sensitive variable is estimated by stratification, and the proportional and optimal allocations are examined as a method of allocating samples to each stratum. Also, in the case of not knowing the size of each stratum, a sensitive variable is estimated by using two-phase sampling, and the method of allocating samples to each stratum is also examined. Also, the efficiency between the proposed stratified model of Murtaza et al.’s and the existing model of Murtaza et al.’s is compared.

Keywords:

• Randomized response model
• Murtaza et al
• ’s model
• sensitive variable
• stratified sampling
• sample allocation

AMS Classification::

• 62D05

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education (2018R1D1A3B07044007).