Statistics

   

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.

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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: 16 times

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