Bayesian Inference for Kendall's Rank Correlation Coefficient
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| Publication date | 2016 |
| Description | 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 (Johnson, 2005). 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. |
| Publisher | Taylor & Francis |
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| Document type | Dataset |
| Related publication | Bayesian Inference for Kendall's Rank Correlation Coefficient |
| DOI | https://doi.org/10.6084/m9.figshare.4452449 |
| Other links | https://tandf.figshare.com/articles/dataset/Bayesian_Inference_for_Kendall_s_Rank_Correlation_Coefficient/4452449 |
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