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University of Illinois at Urbana-Champaign

Modeling Correlated Ordinal Data: Marginal and Conditional Approaches

Abstract

dc:description

General computing algorithms are developed in R for the GEE computations and WinBugs to perform Gibbs sampling for the Bayesian analysis. Analyses of randomized controlled longitudinal data and randomized controlled surgical data are used to illustrate the features of the class of models.

Degree

thesis:*
Name thesis:degree_name
Ph.D.
Level thesis:degree_level
Dissertation
Discipline thesis:degree_discipline
Statistics
Grantor
University of Illinois at Urbana-Champaign
Year dc:date
2015

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Huebner, Alan Randall
Contributors dc:contributor
  • Simpson, Douglas G.

Subjects

dc:subject × 1

Rights

Language dc:language
eng

Identifiers

dc:identifier.*
Identifier
(MiAaPQ)AAI3337799
OAI identifier oai:identifier
oai:www.ideals.illinois.edu:2142/87413

Chain of custody

source
Harvested from
University of Illinois - Urbana-Champaign
Base URL
www.ideals.illinois.edu/oai-pmh
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
citation

Huebner, Alan Randall. Modeling Correlated Ordinal Data: Marginal and Conditional Approaches. Dissertation thesis, University of Illinois at Urbana-Champaign, 2015. http://hdl.handle.net/2142/87413