Two new models for survey sampling with sensitive characteristic: design and analysis
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Publication:745491
DOI10.1007/S00184-007-0131-XzbMath1357.62058OpenAlexW2095421338MaRDI QIDQ745491
Jun-Wu Yu, Man-Lai Tang, Guo-Liang Tian
Publication date: 14 October 2015
Published in: Metrika (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s00184-007-0131-x
maximum likelihood estimatesensitive questionsrandomized response techniquerandomizing deviceWarner's model
Related Items (30)
A variant of the parallel model for sample surveys with sensitive characteristics ⋮ Dealing sensitive characters on successive occasions through a general class of estimators using scrambled response techniques ⋮ Analyzing efficiency for the multi-category parallel method ⋮ Sample size determination for estimating prevalence and a difference between two prevalences of sensitive attributes using the non-randomized triangular design ⋮ A new variant of the parallel regression model with variable selection in surveys with sensitive attribute ⋮ Using randomized response to estimate the population mean of a sensitive variable under the influence of measurement error ⋮ Estimation of the Mean of a Sensitive Variable in the Presence of Auxiliary Information ⋮ A covariate nonrandomized response model for multicategorical sensitive variables ⋮ A survey design for a sensitive binary variable correlated with another nonsensitive binary variable ⋮ Mean estimation of sensitive variables under measurement errors using optional RRT models ⋮ Estimation of parameters of logistic regression for two-stage randomized response technique ⋮ Sample size determination for the parallel model in a survey with sensitive questions ⋮ Estimation of population proportion in randomized response sampling using weighted confidence interval construction ⋮ Assessing the effectiveness of indirect questioning techniques by detecting liars ⋮ Evaluating the sensitive question methods; recommended Uludag Adjustment for the Crosswise Model ⋮ The optimal sample size in the crosswise model for sensitive questions ⋮ A new privacy-protecting survey design for multichotomous sensitive variables ⋮ Valid estimates for repeated randomized response methods ⋮ A new estimator of proportion with a linear function using data from two-decks randomized response model ⋮ Logistic regression analyses for indirect data ⋮ Semiparametric analysis of randomized response data with missing covariates in logistic regression ⋮ Using prior information in privacy-protecting survey designs for categorical sensitive variables ⋮ Exponential-Type Estimators of the Mean of a Sensitive Variable in the Presence of Nonsensitive Auxiliary Information ⋮ A new non-randomized multi-category response model for surveys with a single sensitive question: design and analysis ⋮ Two-sample Non Randomized Response Techniques for Sensitive Questions ⋮ Logistic regression analysis of non‐randomized response data collected by the parallel model in sensitive surveys ⋮ Unbiased estimation of a sensitive proportion in general sampling by three nonrandomized response schemes ⋮ Applying the nonrandomized diagonal model to estimate a sensitive distribution in complex sample surveys ⋮ Sample size determination for interval estimation of the prevalence of a sensitive attribute under randomized response models ⋮ A regression estimator for finite population mean of a sensitive variable using an optional randomized response model
Cites Work
- A stratified Warner's randomized response model.
- A two-stage stratified Warner's randomized response model using optimal allocation
- Optional randomized response in stratified unequal probability sampling -- a simulation based numerical study with Kuk's method
- Randomized Response: A Survey Technique for Eliminating Evasive Answer Bias
- Inadmissibility of Linearly Invariant Estimators in Truncated Parameter Spaces
- Randomized response multivariate designs for categorical data
- Randomized Response: A Data-Gathering Device for Sensitive Questions
- The Efficiency of the Multiple Trial Randomized Response Technique
- A New Model for Randomized Response
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