On Improving the Performance of Logistic Regression Analysis Via Extreme Ranking

Logistic regression models for dichotomous or ordinal dependent variables is one of the generalized linear models. They have been frequently applied in several fields. In this chapter, we present more efficient and powerful performance of the logistic regression models analysis when a modified extreme ranked set sampling (modified ERSS) or moving extreme ranked set sampling (MERSS) are used and further improving the performance when a modified Double extreme ranked set sampling (modified DERSS) is used. We propose that ranking could be performed based on an available and easy to rank auxiliary variable which is associated with the response variable. Analytically and through simulations, we showed the superiority performance of the logistics regression analysis when modified ERSS, MERSS, and DERSS are used compared with using the simple random sample (SRS). For illustration purposes of the procedures developed, we use a real dataset from 2011/12 National Survey of Children’s Health (NSCH)

On Improving the Performance of Logistic Regression Analysis via Extreme Ranking

Invited presentation given at International Symposium on Statistics and Biostatistics (ISBS) at the University of Pretoria

Ratio estimator for the population mean using ranked set sampling

Ratio estimator, Ranked set sampling, Simple random sampling, Efficiency, 62D05,