Reviews:
'The book is modern in outlook and addresses such things
as ‘belief nets’, ‘group decision theory’
and Monte Carlo methods of integration. … this will
be a welcome and useful reference book.' Short Book
Reviews
Key Features:
- Presents a broad picture of the applications of decision
theory
- Examines developments in decision theory
- Introduces the concepts of value and utility theory beyond
those usually found in the statistical literature
- Guides statisticians towards the many and varied skills
they will need in modelling decisions and supporting decision
makers.
Description:
Statistical decision theory provides a framework on which
many statistical procedures may be built and justified. However,
it is not exclusively a subdiscipline of statistics, but provides
models for academic disciplines ranging from history and economics
to psychology, political science and operational research.
Statisticians need to understand the mathematics and techniques
of decision analysis in order that their inferential methods
may interface with the other parts of the analysis.
This addition to the 'Kendall's Library of Statistics' provides
an overview of the main ideas and concepts of statistical
decision theory, and sets it within the broader concept of
decision theory, decision analysis and decision support as
they are practised in many disciplines beyond statistics.
Statistical Decision Theory also guides statisticians towards
the many and varied skills they need for modelling decisions
and supporting decision-makers. It introduces concepts of
value and utility theory beyond those usually found in the
statistical literature, in order that statisticians may become
both belief and preference analysts.
Readership:
Graduate statisticians and those applying decision theory
in a range of other disciplines from psychology to operational
research.
Contents:
Preface
Terminology
1. Decision theory: an overview
2. Axiomatic bases of decision theory
3. Problem structuring, parameters and attributes
4. Group decisions and expert judgement
5. Classical statistical decision theory
6. Bayesian statistical decision theory
7. Decision theoretical computations
8. Sensitivity analysis
9. Sequential statistical decision theory
10. Conclusions
References
Author index
Subject index
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