# Statistical Decision Theory’

#### Statistical Decision Theory

by F. Liese, Klaus-J. Miescke

#### Statistical Decision Theory and Bayesian Analysis

by James O. Berger

#### Introduction to Statistical Decision Theory

by Frank P Ramsey Professor of Managerial Economics (Emeritus) Howard Raiffa, John Winsor Pratt, Robert O. Schlaifer, Howard Raiffa, Robert Schlaifer

*Introduction to Statistical Decision Theory*states the case and in a self-contained, comprehensive way shows how the approach is operational and relevant for real-world decision making under uncertainty.

Starting with an extensive account of the foundations of decision theory, the authors develop the intertwining concepts of subjective probability and utility. They then systematically and comprehensively examine the Bernoulli, Poisson, and Normal (univariate and multivariate) data generating processes. For each process they consider how prior judgments about the uncertain parameters of the process are modified given the results of statistical sampling, and they investigate typical decision problems in which the main sources of uncertainty are the population parameters. They also discuss the value of sampling information and optimal sample sizes given sampling costs and the economics of the terminal decision problems.

Unlike most introductory texts in statistics, *Introduction to Statistical Decision Theory* integrates statistical inference with decision making and discusses real-world actions involving economic payoffs and risks. After developing the rationale and demonstrating the power and relevance of the subjective, decision approach, the text also examines and critiques the limitations of the objective, classical approach.

#### Statistical Decision Theory and Related Topics IV

by Shanti S. Gupta, James O. Berger

#### Statistical Decision Theory

by Nicholas T. Longford

This monograph presents a radical rethinking of how elementary inferences should be made in statistics, implementing a comprehensive alternative to hypothesis testing in which the control of the probabilities of the errors is replaced by selecting the course of action (one of the available options) associated with the smallest expected loss.

Its strength is that the inferences are responsive to the elicited or declared consequences of the erroneous decisions, and so they can be closely tailored to the client’s perspective, priorities, value judgments and other prior information, together with the uncertainty about them.

#### Advances in Statistical Decision Theory and Applications

by S. Panchapakesan, N. Balakrishnan

#### Statistical Decision Theory

by Lionel Weiss

#### Statistical Decision Theory and Related Topics

by Shanti S. Gupta, James Yackel

The conference brought together research workers in decision theory and related topics. This volume contains twenty papers presented during the symposium and includes works on molecular studies of evolution, globally optimal procedure for one-sided comparisons, multiple decision theory, outlier detection, empirical Bayes slippage tests, and non-optimality of likelihood ratio tests for sequential detection of signals in Gaussian noise.

Mathematicians and statisticians will find the book highly insightful.

#### Applied Statistical Decision Theory

by Howard Raiffa, Robert Schlaifer

—From the foreword to their classic work

*Applied Statistical Decision Theory*. First published in the 1960s through Harvard University and MIT Press, the book is now offered in a new paperback edition from Wiley