Dr. Colin M. Gallagher and Dr. Christopher S. McMahan
Group testing through the use of pooling has proven to be an efﬁcient method of reducing the time and cost associated with screening for a binary characteristic of interest such as infection status. A topic of key interest in this area involves the development of regression models that relate the individual level covariates to the binary pool testing responses. The research in this area has primarily focused on parametric regression models. In this poster, we will introduce a new estimation method which can handle multi-dimensional covariates while assuming the link is unknown. The asymptotic properties of our estimators are also presented. We investigate the performance of our method through simulation and by applying it to a hepatitis data set obtained from the National Health and Nutrition Examination Survey.
Wang, Dewei; Kulasekera, Karunarathna B.; Gallagher, Colin M.; and McMahan, Christopher S., "Group testing models with unknown link function" (2013). Graduate Research and Discovery Symposium (GRADS). 79.