Separating Effect From Significance in Markov Chain Tests

We give qualitative and quantitative improvements to theorems which enable significance testing in Markov chains, with a particular eye toward the goal of enabling strong, interpretable, and statistically rigorous claims of political gerrymandering. Our results can be used to demonstrate at a desire...

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Bibliographic Details
Main Authors: Maria Chikina, Alan Frieze, Jonathan C. Mattingly, Wesley Pegden
Format: Article
Language:English
Published: Taylor & Francis Group 2020-01-01
Series:Statistics and Public Policy
Subjects:
Online Access:http://dx.doi.org/10.1080/2330443X.2020.1806763
Description
Summary:We give qualitative and quantitative improvements to theorems which enable significance testing in Markov chains, with a particular eye toward the goal of enabling strong, interpretable, and statistically rigorous claims of political gerrymandering. Our results can be used to demonstrate at a desired significance level that a given Markov chain state (e.g., a districting) is extremely unusual (rather than just atypical) with respect to the fragility of its characteristics in the chain. We also provide theorems specialized to leverage quantitative improvements when there is a product structure in the underlying probability space, as can occur due to geographical constraints on districtings.
ISSN:2330-443X