Overview

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

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9781138083561

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Reviews

A major strength of the book is its linking of decision theory to real-world examples and behaviors, outlining the limitations and alternatives to normative decision theory, while also stressing its strengths and appropriateness in a vast array of situations. Such discussion is particularly valuable in the context of practical applications that imply utility elicitation from individuals. A second major strength is the presence of detailed worked out examples, as well as case studies from either the authors' experience or the literature. As core probability and statistical concepts are reviewed in the earlier chapters, the book is suitable for both students and graduates with a quantitative, although not necessarily statistical, background. The balance between the theoretical exposition and the practical applicability of the concepts makes this book particularly appealing to readers aiming to gain insight into the decision theoretic field for both personal and professional purposes. - Silvia Calderazzo, Appeared in Biometrical Journal, July 2020

Author Information

"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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