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Parameter Redundancy and Identifiability (Hardcover)
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Parameter Redundancy and Identifiability (Hardcover)
Series: Chapman & Hall/CRC Interdisciplinary Statistics
Expected to ship within 10 - 15 working days
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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:
Detailed discussion of the problems caused by parameter redundancy
and non-identifiability Explanation of the different general
methods for detecting parameter redundancy and non-identifiability,
including symbolic algebra and numerical methods Chapter on
Bayesian identifiability Throughout illustrative examples are used
to clearly demonstrate each problem and method. Maple and R code
are available for these examples More in-depth focus on the areas
of discrete and continuous state-space models and ecological
statistics, including methods that have been specifically developed
for each of these areas 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.
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