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An Application of Endpoint Detection to Bivariate Data in Tau-Path Order
Srinath Sampath1, Joseph S Verducci2
1Hamilton Capital Management, Columbus, OH.
Proceedings. American Statistical Association. Annual Meeting
|September 9, 2015
Summary
This study introduces the moving average maximum likelihood estimator (MAMLE) to assess agreement in ranked data, offering a new method for detecting the end of such relationships in biological data.
Area of Science:
- Statistics
- Bioinformatics
- Computational Biology
Background:
- Ranking data analysis is crucial in various scientific fields.
- Existing models may lack the sensitivity to detect the endpoint of agreement in long ranked lists.
- Understanding the duration of relationships in biological data, such as gene expression and compound potency, is important.
Purpose of the Study:
- To modify the Fligner and Verducci (1988) multistage model for rankings.
- To develop a locally smooth estimator, the moving average maximum likelihood estimator (MAMLE).
- To create a stopping rule for detecting the endpoint of agreement in ranked data.
Main Methods:
- Modification of the Fligner and Verducci (1988) multistage ranking model.
- Development and application of the moving average maximum likelihood estimator (MAMLE).
- Application of the MAMLE stopping rule to bivariate data in tau-path order (Yu et al., 2011).
Main Results:
- The MAMLE provides a locally smooth estimation of stage-wise agreement between two long ranked lists.
- The MAMLE stopping rule effectively detects the endpoint of agreement.
- Application to National Cancer Institute data revealed insights into the length of associations between gene expression and compound potency.
Conclusions:
- The MAMLE is a valuable tool for analyzing agreement in ranked data, particularly in bioinformatics.
- The MAMLE stopping rule offers a robust method for identifying the duration of relationships in biological datasets.
- This approach enhances the understanding of gene expression and compound potency associations.
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