Osval Antonio Montesinos Lopez, Statistics and Agronomy, Graduate Student at University of Nebraska-Lincoln, Lincoln, NE and Kent Eskridge, Department of Statistics, University of Nebraska - Lincoln, Lincoln, NE
A regression group testing model for a two-stage survey under an informative sampling for detecting and estimating the presence of transgenic corn
Osval Antonio Montesinos Lopez
Group testing regression methods are effective for estimating and classifying binary responses and can reduce the required number of diagnostic test by 80%. For this reason, these methods have been used for the detection and estimation of transgenic corn in Mexico. However, there is no appropriate methodology when the sampling process is complex and informative. In these cases researchers often ignore the stratification and weights which can severely bias the estimates of the population parameters. In this paper we developed group testing regression models for the analysis of surveys conducted using a two stages with unequal selection probabilities and informative sampling. Weights are incorporated into the likelihood function using the pseudo-likelihood approach. A simulation study demonstrated that the proposed model reduced considerably the bias in estimation compared to other methods that ignore the weights. Finally, we developed a simple example to show the use of the proposed method.
Key words: complex survey, group testing, informative sampling, transgenic corn.