About this scholarly article

Confidence, Likelihood, Probability (Statistical Inference with Confidence Distributions) by Schweder, Tore; Hjort, Nils Lid is a scholarly article available to read on EtoBox.

This lively book lays out a methodology of confidence distributions and puts them through their paces. Among other merits, they lead to optimal combinations of confidence from different sources of information, and they can make complex models amenable to objective and indeed prior-free analysis for less subjectively inclined statisticians. The generous mixture of theory, illustrations, applications and exercises is suitable for statisticians at all levels of experience, as well as for data-oriented scientists. Some confidence distributions are less dispersed than their competitors. This concept leads to a theory of risk functions and comparisons for distributions of confidence. Neyman--Pearson type theorems leading to optimal confidence are developed and richly illustrated. Exact and optimal confidence distribution is the gold standard for inferred epistemic distributions. Confidence distributions and likelihood functions are intertwined, allowing prior distributions to be made part of the likelihood. Meta-analysis in likelihood terms is developed and taken beyond traditional methods, suiting it in particular to combining information across diverse data sources

Author

Schweder, Tore; Hjort, Nils Lid

Publisher

Cambridge University Press (Virtual Publishing)

Published

2016

Language

EN

ISBN

9781139046671