An Application of Endpoint Detection to Bivariate Data in Tau-Path Order

• Activity Number: 340
• Type: Contributed
• Date/Time: Tuesday, August 5, 2014 : 10:30 AM to 12:20 PM
• Sponsor: Section on Nonparametric Statistics
• Author(s): Srinath Sampath*+ and Joseph S. Verducci
• Companies: Ohio State University and Ohio State University
• Keywords: partial rankings ; top-K rank list ; multistage model ; maximum likelihood estimation ; stopping rule ; tau-path

Abstract

The Fligner and Verducci (1988) multistage model for rankings is modified to create the moving average maximum likelihood estimator (MAMLE), a locally smooth estimator that measures stage-wise agreement between two long ranked lists, and provides a stopping rule for the detection of the endpoint of agreement. An application of this MAMLE stopping rule to bivariate data set in tau-path order (Yu, Verducci, and Blower (2011)) is discussed. Data from the National Cancer Institute measuring associations between gene expression and compound potency are studied using this application, providing insights into the length of the relationship between the variables.