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A Comparison of the Bayesian and Frequentist Approaches to Estimation (Hardcover, 2010 ed.)
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A Comparison of the Bayesian and Frequentist Approaches to Estimation (Hardcover, 2010 ed.)
Series: Springer Series in Statistics
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The main theme of this monograph is "comparative statistical
inference. " While the topics covered have been carefully selected
(they are, for example, restricted to pr- lems of statistical
estimation), my aim is to provide ideas and examples which will
assist a statistician, or a statistical practitioner, in comparing
the performance one can expect from using either Bayesian or
classical (aka, frequentist) solutions in - timation problems.
Before investing the hours it will take to read this monograph, one
might well want to know what sets it apart from other treatises on
comparative inference. The two books that are closest to the
present work are the well-known tomes by Barnett (1999) and Cox
(2006). These books do indeed consider the c- ceptual and
methodological differences between Bayesian and frequentist
methods. What is largely absent from them, however, are answers to
the question: "which - proach should one use in a given problem?"
It is this latter issue that this monograph is intended to
investigate. There are many books on Bayesian inference, including,
for example, the widely used texts by Carlin and Louis (2008) and
Gelman, Carlin, Stern and Rubin (2004). These books differ from the
present work in that they begin with the premise that a Bayesian
treatment is called for and then provide guidance on how a Bayesian
an- ysis should be executed. Similarly, there are many books
written from a classical perspective.
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