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Models for complex systems are often built with more parameters than can beuniquely identified by available data. Because of the variety of causes,identifying a lack of parameter identifiability typically requires mathematicalmanipulation of models, monte carlo simulations, and examination of the FisherInformation Matrix. A simple test for parameter estimability is introduced,using Data Cloning, a Markov Chain Monte Carlo based algorithm. Together, Datacloning and the ANOVA based test determine if the model parameters areestimable and if so, determine their maximum likelihood estimates and provideasymptotic standard errors. When not all model parameters are estimable, theData Cloning results and the ANOVA test can be used to determine estimableparameter combinations or infer identifiability problems in the modelstructure. The method is illustrated using three different real data systemsthat are known to be difficult to analyze.

An ANOVA Test for Parameter Estimability using Data Cloning with Application to Statistical Inference for Dynamic Systems