The University of Southampton
University of Southampton Institutional Repository

Statistical methods for the analysis of ordinal response data

Statistical methods for the analysis of ordinal response data
Statistical methods for the analysis of ordinal response data
Ordinal response models and in particular cumulative link models are the most prevalent techniques for modelling ordered response data. This thesis examines the advantages of these models versus other approaches in the socio-economic literature and assesses the performance of available residual diagnostics measures by means of simulation studies and four case studies. Furthermore, it proposes solutions to specific issues of flexible versions of cumulative link models.
University of Southampton
Lorenzo-Arribas, Altea
609bdcec-7c64-49aa-90aa-c54567962ba1
Lorenzo-Arribas, Altea
609bdcec-7c64-49aa-90aa-c54567962ba1
Overstall, Antony
09be306c-8513-46dc-9321-4f3439fbc4cb

Lorenzo-Arribas, Altea (2019) Statistical methods for the analysis of ordinal response data. University of Southampton, Doctoral Thesis, 217pp.

Record type: Thesis (Doctoral)

Abstract

Ordinal response models and in particular cumulative link models are the most prevalent techniques for modelling ordered response data. This thesis examines the advantages of these models versus other approaches in the socio-economic literature and assesses the performance of available residual diagnostics measures by means of simulation studies and four case studies. Furthermore, it proposes solutions to specific issues of flexible versions of cumulative link models.

Text
Final thesis - Version of Record
Available under License University of Southampton Thesis Licence.
Download (3MB)

More information

Published date: 20 June 2019

Identifiers

Local EPrints ID: 436174
URI: http://eprints.soton.ac.uk/id/eprint/436174
PURE UUID: 3774734b-3ff6-46a3-b7cc-3b2da6487831

Catalogue record

Date deposited: 02 Dec 2019 17:31
Last modified: 02 Dec 2019 17:31

Export record

Contributors

Author: Altea Lorenzo-Arribas
Thesis advisor: Antony Overstall

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×