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55 – Teaching Ethics in Statistics and Biostatistics: What Works, What Doesn't Work, and Lessons Learned

How Stable Are Top Choices Over Time? An Investigation into Preferences Among Popular Baby Names in the United States

Sponsor: Section on Nonparametric Statistics
Keywords: Partial Rankings, Top-K Rank List, Multistage Model, Maximum Likelihood Estimation, Stopping Rule, Consensus

Srinath Sampath

The Ohio State University

Joseph S. Verducci

The Ohio State University

For the problem of assessing initial agreement between two rankings of long lists, inference in the Fligner and Verducci (1988) multistage model for rankings is modified to provide a locally smooth estimator of stage-wise agreement. An extension to the case of overlapping but different sets of items in the two lists, and a stopping rule to identify the endpoint of agreement, are also provided. Simulations show that this approach performs very well under several conditions. The methodology is applied to a database of popular names for newborns in the United States and provides insights into trends as well as differences in naming conventions between the two sexes.

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