Rank and linear correlation differences in simulation and other applications Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/3r074x812

Descriptions

Attribute NameValues
Creator
Abstract or Summary
  • Monte Carlo simulation is used to quantify and characterize uncertainty in a variety of applications such as financial/engineering economic analysis, and project management. The dependence or correlation between the random variables modeled can also be simulated to add more accuracy to simulations. However, there exists a difference between how correlation is most often estimated from data (linear correlation), and the correlation that is simulated (rank correlation). In this research an empirical methodology is developed to estimate the difference between the specified linear correlation between two random variables, and the resulting linear correlation when rank correlation is simulated. It is shown that in some cases there can be relatively large differences. The methodology is based on the shape of the quantile-quantile plot of two distributions, a measure of the linearity of the quantile-quantile plot, and the level of correlation between the two random variables. This methodology also gives a user the ability to estimate the rank correlation that when simulated, generates the desired linear correlation. This methodology enhances the accuracy of simulations with dependent random variables while utilizing existing simulation software tools.
Resource Type
Date Available
Date Copyright
Date Issued
Degree Level
Degree Name
Degree Field
Degree Grantor
Commencement Year
Advisor
Committee Member
Academic Affiliation
Non-Academic Affiliation
Keyword
Subject
Rights Statement
Peer Reviewed
Language
Replaces
Additional Information
  • description.provenance : Made available in DSpace on 2013-09-24T18:03:13Z (GMT). No. of bitstreams: 1 AgahiMaryam2013.pdf: 6203511 bytes, checksum: 1dcbff73e1b201933b96bcc56652bf28 (MD5) Previous issue date: 2013-08-09
  • description.provenance : Approved for entry into archive by Laura Wilson(laura.wilson@oregonstate.edu) on 2013-09-24T18:03:13Z (GMT) No. of bitstreams: 1 AgahiMaryam2013.pdf: 6203511 bytes, checksum: 1dcbff73e1b201933b96bcc56652bf28 (MD5)
  • description.provenance : Approved for entry into archive by Julie Kurtz(julie.kurtz@oregonstate.edu) on 2013-09-23T16:15:19Z (GMT) No. of bitstreams: 1 AgahiMaryam2013.pdf: 6203511 bytes, checksum: 1dcbff73e1b201933b96bcc56652bf28 (MD5)
  • description.provenance : Submitted by Maryam Agahi (agahim@onid.orst.edu) on 2013-09-18T16:02:53Z No. of bitstreams: 1 AgahiMaryam2013.pdf: 6203511 bytes, checksum: 1dcbff73e1b201933b96bcc56652bf28 (MD5)

Relationships

In Administrative Set:
Last modified: 08/10/2017

Downloadable Content

Download PDF
Citations:

EndNote | Zotero | Mendeley

Items