Markov-based ranking methods

<p>Ranking methods are an essential tool to help make decisions. This dissertation document examines different aspects of the theory and application of pairwise comparison ranking methods, specifically those that use Markov chains. First, a new method is developed to solve a traditional recruiting problem, and is shown to improve the predictive power of its ranking. Next, modifications are made to an existing method that theoretically improves the reliability, while maintaining the rank integrity. Last, a framework is developed that defines a fair and comprehensive ranking method, and several popular methods are evaluated in their ability to adhere to the said framework.</p>

Name thesis:degree_name

Doctor of Philosophy (PhD)

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Industrial Engineering

Year

2016

Author dc:creator

• Vaziri, Baback

Contributors dc:contributor

• Yuehwern Yih
• Tom Morin
• Mark Lehto
• Robert Plante

• Applied sciences
• Markov chains
• Ranking methods
• Industrial Engineering

Repository record dc:identifier

https://docs.lib.purdue.edu/open_access_dissertations/721

OAI identifier oai:identifier

oai:docs.lib.purdue.edu:open_access_dissertations-1883