Abstract
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
This article is categorized under:
Statistical and Graphical Methods of Data Analysis > Density Estimation
Statistical Learning and Exploratory Methods of the Data Sciences > Modeling Methods
Statistical and Graphical Methods of Data Analysis > Modeling Methods and Algorithms
Algorithms and Computational Methods > Maximum Likelihood Methods