Free access to paper on 'Logistic regression analysis of non‐randomized response data'


  • Author: Statistics Views
  • Date: 08 July 2019

Each week, we select a recently published article and offer either free access or highlight a recent open access publication. This week's is from the Australian & New Zealand Journal of Statistics and is available from Early View.

Logistic regression analysis of non‐randomized response data collected by the parallel model in sensitive surveys

Guo‐Liang Tian, Yin Liu and Man‐Lai Tang

Australian & New Zealand Journal of Statistics , Volume 61, Issue 2, June 2019, pages 134-151


thumbnail image: Free access to paper on 'Logistic regression analysis of non‐randomized response data'

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.

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