| Creators |
|
| Contributors |
|
| 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
|
| Organisations |
-
Faculty of Social and Behavioural Sciences (FMG) - Psychology Research Institute (PsyRes)
|
| Document type |
Dataset
|
| Related publication |
Bayesian Inference for Kendall's Rank Correlation Coefficient
|
| DOI |
https://doi.org/10.6084/m9.figshare.4452449 |
| Other links |
|
|
Permalink to this page
|
