Chapman and Hall/CRC
280 pages | 34 B/W Illus.
Statistical and mathematical models are defined by parameters that describe different characteristics of those models. Ideally it would be possible to find parameter estimates for every parameter in that model, but, in some cases, this is not possible. For example, two parameters that only ever appear in the model as a product could not be estimated individually; only the product can be estimated. Such a model is said to be parameter redundant, or the parameters are described as non-identifiable. This book explains why parameter redundancy and non-identifiability is a problem and the different methods that can be used for detection, including in a Bayesian context.
Key features of this book:
This book is designed to make parameter redundancy and non-identifiability accessible and understandable to a wide audience from masters and PhD students to researchers, from mathematicians and statisticians to practitioners using mathematical or statistical models.
2. Problems With Parameter Redundancy
3. Parameter Redundancy and Identifiability Definitions and Theory
4. Practical General Methods for Detecting Parameter Redundancy and Identifiability
5. Detecting Parameter Redundancy and Identifiability in Complex Models
6. Bayesian Identifiability
7. Identifiability in Continuous State-Space Models
8. Identifiability in Discrete State-Space Models
9. Detecting Parameter Redundancy in Ecological Models
10. Concluding Remarks
Appendix A. Maple Code
Appendix B. Winbugs and R Code