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Introduction to Statistical Decision Theory

Silvia Bacci | Bruno Chiandotto
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A01=Bruno Chiandotto
A01=Silvia Bacci
advanced statistical decision theory applications
Author_Bruno Chiandotto
Author_Silvia Bacci
Bayesian
Bayesian inference methods
Bayesian statistical decision theory
Category=KCH
Category=PBT
Causal Decision Theory
causal inference
causal inference techniques
causality
CE
Classical Decision Theory
classical statistical decision theory
Criterion Decision Methods
decision analysis frameworks
Decision Function
Decision Table
Descriptive Decision Theory
eq_bestseller
eq_business-finance-law
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
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European Customer Satisfaction Index
Expected Utility Principle
Expected Utility Theory
Hurwicz's Criterion
Hurwicz’s Criterion
Independence Axiom
Indian ISBN
Informational Background
Max Min Criterion
Maximum Surface Wind Speed
Normative Decision Theory
Prior Information
Rank Dependent Utility Theory
Recursive Path Models
Recursive SEM
risk
risk analysis modeling
Standard Gamble Methods
Swedish Customer Satisfaction Barometer
Tv Device
Tv User
uncertainty
uncertainty quantification
Utility Function
utility function assessment
value theory

Product details

  • ISBN 9781032091754
  • Weight: 420g
  • Dimensions: 156 x 234mm
  • Publication Date: 30 Jun 2021
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
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Description

Introduction to Statistical Decision Theory: Utility Theory and Causal Analysis provides the theoretical background to approach decision theory from a statistical perspective. It covers both traditional approaches, in terms of value theory and expected utility theory, and recent developments, in terms of causal inference. The book is specifically designed to appeal to students and researchers that intend to acquire a knowledge of statistical science based on decision theory.

Features



  • Covers approaches for making decisions under certainty, risk, and uncertainty




  • Illustrates expected utility theory and its extensions




  • Describes approaches to elicit the utility function




  • Reviews classical and Bayesian approaches to statistical inference based on decision theory




  • Discusses the role of causal analysis in statistical decision theory
About Bruno Chiandotto | Silvia Bacci

Silvia Bacci is Assistant Professor of Statistics at the Department of Statistics, Computer Science and Applications "G. Parenti", University of Florence (Italy). Her research interests are addressed to statistical decision theory, with focus on utility theory, and latent variable models, with focus on item response theory models, latent class models, and models for longitudinal and multilevel data.

Bruno Chiandotto is adjunct Full Professor of Statistics at the Department of Statistics, Computer Science and Applications "G. Parenti", University of Florence (Italy). He is mainly interested in the definition and estimation of linear and nonlinear statistical models, multivariate data analysis, customer satisfaction, causal analysis, statistical decision theory and utility theory. A large part of his research activity has been carried out under projects funded by international, national and local institutions.

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