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Course Bibliography
Multivariate Analysis
Multivariate Analysis (2017)
  • Bennie Liu
Description
Multivariate analysis (MVA) is founded on the statistical principle of multivariate statistics, which involves simultaneous observation and analysis of more than two statistical outcome variables at a time. Multivariate analysis can be complex by the desire to include physics-based analysis to measure the effect of variables for a hierarchical "system-of-systems". Studies aimed at multivariate analysis are often stalled by the dimensionality of the problem. These concerns are frequently eased by using surrogate models, highly accurate approximations of the physics-based code. Since surrogate models take the form of an equation, they can be estimated very quickly. This makes it possible for large-scale MVA: while a Monte Carlo simulation across the design space is very difficult with physics-based codes, it becomes worthless when evaluating surrogate models, which frequently take the form of response-surface equations. Multivariate statistics has been a useful tool for the analysis of metabolomic data.
Keywords
  • Multivariate Analysis,
  • creative proteomics
Disciplines
Publication Date
Winter November 4, 2017
Citation Information
Bennie Liu. "Multivariate Analysis" Multivariate Analysis (2017)
Available at: http://works.bepress.com/Bioarray/8/