Rank Regression with Normal Residuals using the Gibbs Sampler

Authors: Stephen P. Smith

Yu (2000) described the use of the Gibbs sampler to estimate regression parameters where the information available in the form of depended variables is limited to rank information, and where the linear model applies to the underlying variation beneath the ranks. The approach uses an imputation step, which constitute nested draws from truncated normal distributions where the underlying variation is simulated as part of a broader Bayesian simulation. The method is general enough to treat rank information that represents ties or partial orderings.

Comments: 9 Pages.

Download: PDF

Submission history

[v1] 2018-02-14 15:07:02 (removed)
[v2] 2018-02-15 12:50:14 (removed)
[v3] 2018-02-16 14:52:45

Unique-IP document downloads: 28 times is a pre-print repository rather than a journal. Articles hosted may not yet have been verified by peer-review and should be treated as preliminary. In particular, anything that appears to include financial or legal advice or proposed medical treatments should be treated with due caution. will not be responsible for any consequences of actions that result from any form of use of any documents on this website.

Add your own feedback and questions here:
You are equally welcome to be positive or negative about any paper but please be polite. If you are being critical you must mention at least one specific error, otherwise your comment will be deleted as unhelpful.

comments powered by Disqus