Modes of Parametric Statistical Inference (Wiley Series in Probability and Statistics Book 723) 1st Edition by Seymour Geisser | (PDF) Free Download

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A fascinating investigation into the foundations of statistical inference This publication examines the distinct philosophical foundations of different statistical modes of parametric inference. Unlike many other texts that focus on methodology and applications, this book focuses on a rather unique combination of theoretical and foundational aspects that underlie the field of statistical inference. Readers gain a deeper understanding of the evolution and underlying logic of each mode as well as each mode’s strengths and weaknesses. The book begins with fascinating highlights from the history of statistical inference. Readers are given historical examples of statistical reasoning used to address practical problems that arose throughout the centuries. Next, the book goes on to scrutinize four major modes of statistical inference: * Frequentist * Likelihood * Fiducial * Bayesian The author provides readers with specific examples and counterexamples of situations and datasets where the modes yield both similar and dissimilar results, including a violation of the likelihood principle in which Bayesian and likelihood methods differ from frequentist methods. Each example is followed by a detailed discussion of why the results may have varied from one mode to another, helping the reader to gain a greater understanding of each mode and how it works. Moreover, the author provides considerable mathematical detail on certain points to highlight key aspects of theoretical development. The author’s writing style and use of examples make the text clear and engaging. This book is fundamental reading for graduate-level students in statistics as well as anyone with an interest in the foundations of statistics and the principles underlying statistical inference, including students in mathematics and the philosophy of science. Readers with a background in theoretical statistics will find the text both accessible and absorbing.

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⭐Seymour Geisser was a wonderful statistician who was very knowledgeable about statistical inference and its historical development. It is unfortunate that he died before the completion of the book but he was aware that he might not survive to completion of the book. So he enlisted the help of his colleague and former student Wesley Johnson who mananged the difficult task of completing the book while preserving Seymours ideas and style.I was a graduate student in 1977 when Seymour visited Stanford (I believe I have the year right although it could have been 1978). That year we held a seminar course on rereading R. A. Fisher. Seymour participated in the seminar. For credit in the course my assigment was to read about the Behren’s-Fisher problem and the issues and controversy that it raised when comparing Neyman’s frequentist approach to hypothesis testing with Fisher’s fiducial approach. The problem of estimating means from normal distributions when the variances were unknown but could not be assumed to be equal exposed the differences between the two approaches and was more or less the problem that led to the downfall of fiducial inference although Fisher fought for it the rest of his life. My recollection of Seymour was that he was very aware of this problem and the eccentricacies of the arguments. He asked me some very insightful questions and I was not able to answer all of them.This detailed knowledge is evident in this book in his treatment of the Behren’s Fisher problem and his ability to show the strengths and weaknesses of all modes through practical examples.This book is written in a very engaging manner but is not your typical theoretical statistics book. It provides Seymour’s expert understanding of the histroy of statistical inference and focuses and the foundations. Statistics is a field with multiple approaches to inference. The Bayesian and frequentist approaches have survived and when properly implimented genrally lead to similar conclusions but with philosophical differences that no longer seem to be as important or controversial as the once did to statisticians. Seymour beleived that the value of statistics comes from the ability of the inference method to accurately predict future results based on past data.

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