Logistic regression analysis of non-randomized response data collected by the parallel model in sensitive surveys
Logistic regression analysis of non-randomized response data collected by the parallel model in sensitive surveys
复制标题
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
DOI：
10.1111/anzs.12258
复制
发表时间：
2019
影响因子：
1.1
通讯作者：
Tang Man-Lai
中科院分区：
文献类型：
--
作者：
Tian Guo-Liang;Liu Yin;Tang Man-Lai
为了研究敏感二元响应变量与一组非敏感协变量之间的关系，本文开发了一种隐逻辑回归来分析通过Tian（2014）最初提出的平行模型收集的非随机响应数据。这是第一篇在非随机应答技术领域使用逻辑回归分析的论文。Newton-Raphson算法和单调二次下界算法的开发，以获得感兴趣的参数的最大似然估计。特别是，所提出的逻辑平行模型可以用于通过比值比的测量来研究敏感二元变量与另一个非敏感二元变量之间的关联。模拟进行，并在美国的人的性行为数据的研究被用来说明所提出的方法。
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.