272 Pages
34 B/W Illustrations
by
Chapman & Hall
272 Pages
34 B/W Illustrations
by
Chapman & Hall
272 Pages
34 B/W Illustrations
by
Chapman & Hall
Also available as eBook on:
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... Read more
1. Introduction
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
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
Biography
Diana Cole is a Senior Lecturer in Statistics at the University of Kent. She has written and co-authored 15 papers on parameter redundancy and identifiability, including general theory and ecological applications.
"This is an interesting book which concentrates on a relatively narrow, but certainly important and unfortunately often neglected topic of identifiability in statistical (and generic mathematical) models...In principle, it is certainly accessible to a wide audience, from students to practicing statisticians, or even to quantitatively oriented non-statistical scientists...Very nicely, the book reads somewhat as a story, going from simpler things to the more complicated, ultimately leading to fascinating and far-reaching things like design considerations with respect to extrinsic parameter redundancy, as well as practical implications for what the author calls integrated population models."
- Marek Brabec, ISCB News, December 2020
