To facilitate the design of efficient simulation experiments, Schruben and Margolin (1978) recommend a correlation induction strategy for orthogonally blockable experimental designs. The objective of such experiments is to estimate a general linear regression model on the basis of a quantitative response variable generated by the simulation model. Nozari, Arnold, and Pegden (1987) develop optimal statistical procedures for analyzing simulation experiments performed under the Schruben-Margolin correlation induction strategy. Formulas are given for parameter estimation, hypothesis testing, and confidence interval estimation. The validity of this statistical analysis procedure is contingent upon the presence of a pure error component in the response. The goal of this thesis is to provide an appropriate statistical analysis technique for simulation experiments conducted under the Schruben-Margolin correlation induction strategy in the absence of pure error, and to identify conditions under which the pure error component is absent.
Often, in order to construct valid inferences on the responses from a simulation experiment, the technique used to execute the simulation experiment must be properly identified. For purposes of this research, the identification problem takes the form of ensuring that the hypothesized metamodel is appropriate for the number of random number streams used to induce correlations between responses across design points. / Master of Science
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/43895 |
| Date | 24 July 2012 |
| Creators | Crenshaw, Marnita Delrae |
| Contributors | Industrial Engineering and Operations Research, Tew, Jeffrey D., Schmidt, Joseph W., Greene, Timothy J. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis, Text |
| Format | viii, 104 leaves, BTD, application/pdf, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
| Relation | OCLC# 20590694, LD5655.V855_1989.C746.pdf |
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