Bayesian Inference for Kendall's Rank Correlation Coefficient
| Authors | |
|---|---|
| Publication date | 2018 |
| Journal | American Statistician |
| Volume | Issue number | 72 | 4 |
| Pages (from-to) | 303-308 |
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| Abstract | This article outlines a Bayesian methodology to estimate and test the Kendall rank correlation coefficient τ. The nonparametric nature of rank data implies the absence of a generative model and the lack of an explicit likelihood function. These challenges can be overcome by modeling test statistics rather than data. We also introduce a method for obtaining a default prior distribution. The combined result is an inferential methodology that yields a posterior distribution for Kendall’s τ. |
| Document type | Article |
| Note | With supplementary materials |
| Language | English |
| Related dataset | Bayesian Inference for Kendall's Rank Correlation Coefficient |
| Published at |
https://doi.org/10.1080/00031305.2016.1264998
(Final published version)
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| Other links | |
| Downloads |
Bayesian Inference for Kendall s Rank Correlation Coefficient
(Final published version)
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| Supplementary materials | |
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