Logistic regression analyses for indirect data |
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Authors: | Heiko Groenitz |
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Institution: | 1. School of Business and Economics, Philipps-University Marburg, Working Group Statistics, Universit?tsstra?e, Marburg, Germanygroenitz@staff.uni-marburg.de |
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Abstract: | The article’s topic is logistic regression for direct data on the covariates, but indirect data on the endogenous variable. The indirect data may result from a privacy-protecting survey procedure for sensitive characteristics or from statistical disclosure control. Various procedures to generate the indirect data exist. However, we show that it is possible to develop a general approach for logistic regression analyses with indirect data that covers many procedures. We first derive a general algorithm for the maximum likelihood estimation and a general procedure for variance estimation. Subsequently, lots of examples demonstrate the broad applicability of our general framework. |
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Keywords: | Item count technique Missing data Non-randomized response Randomized response Sensitive question Statistical disclosure control |
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