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首页> 外文期刊>Australian & New Zealand journal of statistics >Logistic regression analysis of non-randomized response data collected by the parallel model in sensitive surveys
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Logistic regression analysis of non-randomized response data collected by the parallel model in sensitive surveys

机译：敏感调查中并行模型收集的非随机响应数据的逻辑回归分析

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To study the relationship between a sensitive binary response variable and a set of non-sensitive covariates, this paper develops a hidden logistic regression to analyse non-randomized response data collected via the parallel model originally proposed by Tian (2014). This is the first paper to employ the logistic regression analysis in the field of non-randomized response techniques. Both the Newton-Raphson algorithm and a monotone quadratic lower bound algorithm are developed to derive the maximum likelihood estimates of the parameters of interest. In particular, the proposed logistic parallel model can be used to study the association between a sensitive binary variable and another non-sensitive binary variable via the measure of odds ratio. Simulations are performed and a study on people's sexual practice data in the United States is used to illustrate the proposed methods.
机译：为了研究敏感的二元响应变量和一组非敏感的协变量之间的关系，本文开发了一种隐藏的逻辑回归，以分析通过田（2014）最初提出的并行模型收集的非随机响应数据。这是第一篇在非随机响应技术领域中采用逻辑回归分析的论文。牛顿-拉夫森算法和单调二次下界算法都被开发来导出感兴趣参数的最大似然估计。特别地，所提出的逻辑平行模型可以用于通过比值比的量度来研究敏感二进制变量和另一个非敏感二进制变量之间的关联。进行了模拟，并通过对美国人们的性行为数据进行的研究来说明所提出的方法。

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