Semiparametric analysis of randomized response data with missing covariates in logistic regression

Hsieh, Shu Hui; Lee, S. M.; Shen, Pao‐Sheng

doi:10.1016/j.csda.2009.01.011

Cited by 22 publications

(15 citation statements)

References 21 publications

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“…Next, data were collected to study the proportion of unauthorized cable TV users in Taichung, Yunlin, and Taipei, Taiwan in 2004, and were analyzed by Hsieh et al (2009). The respondents were required to answer one of the questions of unrelated-question RRT below based on the last digit of their identification number.…”

Section: A Special Case Of the Proposed Methods And Design Considerationmentioning

confidence: 99%

“…We studied the proportion of unauthorized cable TV users in Taichung, Yunlin, and Taipei, Taiwan in 2004, from Hsieh et al (2009). Because unauthorized cable TV use is illegal and a sensitive question, the unrelated-question RRT design was used in this telephone interview survey.…”

Section: Examplementioning

confidence: 99%

“…Corstange (2004) proposed a method to estimate the parameters of a hidden logistic regression model. Hsieh et al (2009) proposed two semiparametric approaches to estimate the parameters of a logistic regression model with missing covariates for an RRT model. Kim and Heo (2013) combined the two most celebrated randomized response techniques, which are the related-question RRT of Warner (1965) and the unrelated-question RRT of Greenberg et al (1969) with group testing.…”

Section: Introductionmentioning

confidence: 99%

“…Our work is motivated by the case of the two existing survey study data examples, an extramarital relationship study from the TSCS and a cable TV study on unauthorized cable TV connection in Taiwan by Hsieh et al (2009), in which answers of an innocuous question from the unrelated-question RRT can be indirectly and completely observed from answers of a demographic question that is related to the innocuous question. To improve the protection of privacy, we consider the joint use of the unrelated-question RRT of Greenberg et al (1969) and the related-question RRT of Warner (1965) dealing with the issue of an innocuous question from the unrelated-question RRT.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An alternative to unrelated randomized response techniques with logistic regression analysis

Hsieh

Lee

et al. 2016

Stat Methods Appl

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The randomized response technique (RRT) is an important tool that is commonly used to protect a respondent’s privacy and avoid biased answers in surveys on sensitive issues. In this work, we consider the joint use of the unrelated-question RRT of Greenberg et al. (J Am Stat Assoc 64:520–539, 1969) and the related-question RRT of Warner (J Am Stat Assoc 60:63–69, 1965) dealing with the issue of an innocuous question from the unrelated-question RRT. Unlike the existing unrelated-question RRT of Greenberg et al. (1969), the approach can provide more information on the innocuous question by using the related-question RRT of Warner (1965) to effectively improve the efficiency of the maximum likelihood estimator of Scheers and Dayton (J Am Stat Assoc 83:969–974, 1988). We can then estimate the prevalence of the sensitive characteristic by using logistic regression. In this new design, we propose the transformation method and provide large-sample properties. From the case of two survey studies, an extramarital relationship study and a cable TV study, we develop the joint conditional likelihood method. As part of this research, we conduct a simulation study of the relative efficiencies of the proposed methods. Furthermore, we use the two survey studies to compare the analysis results under different scenarios.

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Section: A Special Case Of the Proposed Methods And Design Considerationmentioning

confidence: 99%

Section: Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An alternative to unrelated randomized response techniques with logistic regression analysis

Hsieh

Lee

et al. 2016

Stat Methods Appl

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“…The idea of the simple inverse probability weighted (SIPW) estimator is to adjust the CC analysis by using the inverse of the selection probability as the weight. The SIPW estimator and some semiparametric estimators were applied to randomized response data with missing covariates in logistic regression . In additional to weighting, a missing variable can be imputed from the mean conditional on all observed variables, or some of the variables that are related to the missing variable.…”

Section: Introductionmentioning

confidence: 99%

Best linear inverse probability weighted estimation for two‐phase designs and missing covariate regression

Wang

Dai

2019

Statistics in Medicine

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The inverse probability weighted estimator is often applied to two‐phase designs and regression with missing covariates. Inverse probability weighted estimators typically are less efficient than likelihood‐based estimators but, in general, are more robust against model misspecification. In this paper, we propose a best linear inverse probability weighted estimator for two‐phase designs and missing covariate regression. Our proposed estimator is the projection of the SIPW onto the orthogonal complement of the score space based on a working regression model of the observed covariate data. The efficiency gain is from the use of the association between the outcome variable and the available covariates, which is the working regression model. One advantage of the proposed estimator is that there is no need to calculate the augmented term of the augmented weighted estimator. The estimator can be applied to general missing data problems or two‐phase design studies in which the second phase data are obtained in a subcohort. The method can also be applied to secondary trait case‐control genetic association studies. The asymptotic distribution is derived, and the finite sample performance of the proposed estimator is examined via extensive simulation studies. The methods are applied to a bladder cancer case‐control study.

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Estimation of a zero-inflated Poisson regression model with missing covariates via nonparametric multiple imputation methods

Lee

Lukusa

2019

Comput Stat

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Semiparametric analysis of randomized response data with missing covariates in logistic regression

Cited by 22 publications

References 21 publications

An alternative to unrelated randomized response techniques with logistic regression analysis

An alternative to unrelated randomized response techniques with logistic regression analysis

Best linear inverse probability weighted estimation for two‐phase designs and missing covariate regression

Estimation of a zero-inflated Poisson regression model with missing covariates via nonparametric multiple imputation methods

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