Response modeling methodology
Article first published online: 25 MAR 2011
Copyright © 2011 John Wiley & Sons, Inc.
Wiley Interdisciplinary Reviews: Computational Statistics
Volume 3, Issue 4, pages 357–372, July/August 2011
How to Cite
Shore, H. (2011), Response modeling methodology. WIREs Comp Stat, 3: 357–372. doi: 10.1002/wics.151
- Issue published online: 3 JUN 2011
- Article first published online: 25 MAR 2011
- distribution fitting;
- empirical modeling;
- Response modeling methodology;
- nonlinear regression;
- systematic variation
Response modeling methodology (RMM) is a general platform for modeling monotone convex relationships. Unique to RMM models is their ‘continuous convexity’ property, which allows the data to ‘select’ the final form of the model via the estimated parameters (analogously with the Box–Cox transformation). This renders RMM a versatile and effective platform for empirical modeling of random variation (‘distribution fitting’) and of systematic variation (‘relational modeling’). In this overview, we detail the motivation that led to the development of RMM, explain RMM core concepts, and introduce RMM basic model and variations. Allied maximum-likelihood estimation procedures are detailed, separately for models of random variation and for models of systematic variation. Numerical examples demonstrate RMM effectiveness in comparison to other current approaches. Current literature on RMM (about 25 publications), available software, and ongoing research are also addressed. WIREs Comp Stat 2011 3 357–372 DOI: 10.1002/wics.151
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