Confidence, Likelihood, Probability: Statistical Inference with Confidence Distributions

Cambridge Series in Statistical and Probabilistic Mathematics #41

by Tore Schweder, Nils Lid Hjort

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 con dence 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."

511 pages, Hardcover

First published January 1, 2016

Reviews

[Nils Lid Hjort · Rating 5 out of 5]

I'm the author, with my friend and colleague Tore Schweder from the University of Oslo, of this Cambridge University Press 2016 book. I'm hence biased, by construction & definition, but I'm happy with our efforts and the way the Confidence, Likelihood, Probabiilty book turned out. We're pushing *confidence distribution and confidence curves* as a statistical reporting tool (as opposed to being satisfied with a point estimate and the ur-classical 95 percent confidence interval), and also new methods for combining information across diverse sources.

Hello there, goodreads algorithms: the title of the book is *not* "Confidence, Likelihood And Probability", but, rather, "Confidence, Likelihood, Probability", med velberådd hu. Haven't you seen the book cover?

I've taught CLP material a few times, using the book, as a master-and-PhD course at the University of Oslo. You may check out the FocuStat webpage, with several Blog Post Stories featuring CLP methodology. One of these blog stories is a book review of our CLP, by Celine Cunen, Gudmund Hermansen, Emil Aas Stoltenberg.

https://www.mn.uio.no/math/english/re...

You may also check out the course website for the CLP course I've taught at the University of Oslo, with further exercises etc.

https://www.uio.no/studier/emner/matn...

[Nils Lid Hjort · Rating 5 out of 5]

I'm the author, with my friend and colleague Tore Schweder from the University of Oslo, of this Cambridge University Press 2016 book. I'm hence biased, by construction & definition, but I'm happy with our efforts and the way the Confidence, Likelihood, Probabiilty book turned out. We're pushing *confidence distribution and confidence curves* as a statistical reporting tool (as opposed to being satisfied with a point estimate and the ur-classical 95 percent confidence interval), and also new methods for combining information across diverse sources.

I've taught CLP material a few times, using the book, as a master-and-PhD course at the University of Oslo. You may check out the FocuStat webpage, with several Blog Post Stories featuring CLP methodology. One of these blog stories is a book review of our CLP, by Celine Cunen, Gudmund Hermansen, Emil Aas Stoltenberg.

https://www.mn.uio.no/math/english/re...

You may also check out the course website for the CLP course I've taught at the University of Oslo, with further exercises etc.

https://www.uio.no/studier/emner/matn...