This paper is from Session 4C: Rank-based inference, association measures and nonparametric statistics

which comes under Topic 4: Statistics education at the post-secondary level

Combining nonparametric inferences using data depth, bootstrap and confidence distribution

Presenter

• Dungang Liu (Yale University, United States)

Co-authors

• Min-ge Xie (Rutgers University, United States)
• Regina Y Liu (Yale University, United States)

Abstract

For the purpose of combining inferences from several nonparametric studies for a common hypothesis, we develop a new methodology using the concepts of data depth and confidence distribution (CD). In recent years, the concept of CD has attracted renewed interest and has shown high potential to be an effective tool in statistical inference. In this project, we use the concept of CD, coupled with data depth, to develop a new approach for combining the test results from several independent studies for a common multivariate nonparametric hypothesis. Specifically, in each study, we apply data depth and bootstraps to obtain a p-value function for the common hypothesis. The p-value functions are then combined under the framework of combining confidence distributions. The method will be illustrated using simulations and aircraft landing performance data.