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Quantitative Modeling Methods for Analyzing Clinical to Public Health Problems


Abstract Statistical Methods have been widely used in understanding factors for clinical and public health data. Statistical hypotheses are procedures for testing pre-stated hypotheses. The development and properties of these procedures as well as their performance are based upon certain assumptions. Desirable properties of statistical tests are to maintain validity and to perform well even if these assumptions are not met. A statistical test that maintains such desirable properties is called robust. Mathematical models are typically mechanistic framework, used to study dynamic interactions between components (mechanisms) of a system, and how these interactions give rise to the changes in behavior (patterns) of the system as a whole over time.

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Created Date 2015
Contributor Gonzalez, Beverly (Author) / Castillo-Chavez, Carlos (Advisor) / Mubayi, Anuj (Advisor) / Nuno, Miriam (Committee member) / Arizona State University (Publisher)
Subject Mathematics / Biostatistics
Type Doctoral Dissertation
Extent 132 pages
Language English
Copyright
Reuse Permissions All Rights Reserved
Note Doctoral Dissertation Applied Mathematics for the Life and Social Sciences 2015
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


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Description Dissertation/Thesis