• Journal of applied mathematics & informatics
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• v.9 no.2
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• pp.825-835
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• 2002

In this paper, we extend the conditional unrelated question model which was suggested by Lee and Hong(2000) to two-sample case when there is no information about the true proportion of the unrelated character Y. Conditions are obtained under which the proposed model is more efficient than Carr et al.\`s conditional modal and Greenberg et al.'s two-sample unrelated question model.

• Communications for Statistical Applications and Methods
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• v.8 no.3
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• pp.753-765
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• 2001

We suggest a quantitative conditional unrelated question model that can be used in obtaining more sensitive information. For whom say "yes" about the less 7han sensitive question .B we ask only about the more sensitive variable X. We extend our model to two sample case when there is no information about the true mean of the unrelated variable Y. Finally we compare the efficiency of our model with that of Greenberg et al.′s.

• The Korean Journal of Applied Statistics
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• v.13 no.2
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• pp.575-590
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• 2000

In this paper, we extended the Kim et al.'s two-stage unrelated question model(1992) and the Lee et al.'s improved unrelated question model(1998) to two sample unrelated question model of using two independent samples in the case of unknown $\pi_y$.

• The Korean Journal of Applied Statistics
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• v.26 no.2
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• pp.209-222
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• 2013

We apply stratified cluster sampling to a replicated systematic unrelated question model for a large scale survey in which the population is comprised of several strata developed by several clusters and with sensitive parameters. We first present a replicated systematic unrelated question model using an unrelated question model to procure sensitive information from the population of clusters and then develop a suggested model to an unrelated question by a stratified cluster replicated systematic sampling that can be used in large population of strata. We cover the proportional and optimum allocation for the suggested model. Finally, we compare and analyze the efficiency of the suggested model with the replicated systematic unrelated question model.

• Journal of applied mathematics & informatics
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• v.8 no.1
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• pp.253-260
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• 2001

In this paper we suggest a conditional unrelated question randomized response model by using the Carr et. al.’s model(1982) and Greenberg et. al.’s model(1969). Our model can obtain more comprehensive information about the sensitive character A. We suggest the conditions that make our model efficient compared with models of Greenberg et. al. and Carr et al..

• The Korean Journal of Applied Statistics
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• v.11 no.2
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• pp.415-421
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• 1998

In this paper, we proposed improved unrelated question model which has the benefit of simplicity the Kim et al.'s two-stage unrelated question model(1992). conditions are obtained under which the proposed model is more efficient than the Greenberg et al. model(1971) and Kim et al's two-stage unrelated question model.

• Communications for Statistical Applications and Methods
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• v.18 no.4
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• pp.423-431
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• 2011

For procuring more sensitive information and estimating stratum target population proportion as well as an overall one form a sensitive population composed of several strata we suggest a two-stage stratified unrelated question model that uses stratified random sampling instead of simple random sampling in the two-stage unrelated question model by Kim et al. (1992) and extend it to the three-stage stratified unrelated question model. We also deal with the proportional and optimal allocation problems in each suggested model, compare the relative efficiency of the suggested two models, and show that the three-stage stratified unrelated question model is more efficient than the two-stage one in view of the variance.

• Journal of the Korean Data Analysis Society
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• v.20 no.6
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• pp.2883-2893
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• 2018

We suggest a stratified conditional unrelated question randomized response model to more efficiently estimate a sensitive character A when the population is composed of several strata. In that model, only the respondents who answered "yes" through randomization device which was consisted of a less sensitive character B and a question forcing to answer "yes" respond to our suggested model and we deal with two allocation problems of proportional allocation and optimal one. We expand the suggested model into two sample stratified conditional unrelated question model to cover the case of unknowing unrelated character and deduce minimal variance through optimal sample size of stratum h. Finally, we show that the suggested model is more efficiency than stratified unrelated models and the stratified Carr et al.'s model (1982) under some given conditions, and show numerically that the smaller the values ${\pi}_{h2}$ and ${\pi}_{hy}$, the more efficiency the fit of the model.

• The Korean Journal of Applied Statistics
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• v.8 no.1
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• pp.27-38
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• 1995

In the surveys of sensitive issues of the population that is composed of several unknown-size stratum, we propose the unrelated question model with quantitative attribute by using stratified double sampling. And, we consider two types of sample allocations under the fixed cost, which are the proportional allocation, the optimum allocation. In efficiency, the proosed model is inferior to the unrelated question model with quantitative attribute by stratified sampling in case of the size of each stratum is known. But we find that efficiency of the proposed model is increased, when the selecting probability of sensitive question p is small and first stage sample size n' is large.

• Journal of the Korean Data and Information Science Society
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• v.14 no.1
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• pp.55-65
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• 2003

In this study, we systemize the theoretical validity for applying unrelated question model to three-stage cluster sampling method and derive the estimate and it's variance of sensitive parameter. We derive the minimum variance form under the optimal values of the subsample sizes when the cost are fixed. Under the some given precision, we obtain the optimal values of the subsample sizes and derive the minimum cost form by using them.