Separating Effect From Significance in Markov Chain Tests-Reference-Cited by-同舟云学术

Separating Effect From Significance in Markov Chain Tests

Published:2020-01-01 Issue:1 Volume:7 Page:101-114
ISSN:2330-443X
Container-title:Statistics and Public Policy
language:en
Short-container-title:Statistics and Public Policy

Author:

Chikina Maria¹,Frieze Alan²,Mattingly Jonathan C.³,Pegden Wesley²

Affiliation:

1. Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA;

2. Department of Mathematical Sciences, Carnegie Mellon University, Pittsburgh, PA;

3. Department of Mathematics, Duke University, Durham, NC

Publisher

Informa UK Limited

Subject

Applied Mathematics,Statistics, Probability and Uncertainty,Public Administration,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/2330443X.2020.1806763

Reference5 articles.

1. Generalized Monte Carlo significance tests

2. Optimal Legislative County Clustering in North Carolina

3. Assessing significance in a Markov chain without mixing

4. Herschlag, G. , Kang, H. S. , Luo, J. , Graves, C. V. , Bangia, S. , Ravier, R. , and Mattingly, J. C. (2018), “Quantifying Gerrymandering in North Carolina,” arXiv no. 1801.03783.

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