Principles of Statistical Inference from a Neo-Fisherian Perspective

Luigi Pace|Alessandra Salvan

ISBN 10: 9810230664 / ISBN 13: 9789810230661

Published by WSPC, 1997

About this Item

Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. In this book, an integrated introduction to the statistical inference is provided from a frequentist likelihood-based viewpoint. Classical results are presented together with recent developments largely built upon ideas due to R.A. Fisher.Inhaltsver.

Seller Inventory # 449933723

Bibliographic Details

• Title: Principles of Statistical Inference from a Neo-Fisherian Perspective
• Publisher: WSPC
• Publication Date: 1997
• Language: English
• ISBN 10: 9810230664
• ISBN 13: 9789810230661
• Binding: Gebunden
• Condition: New

About this title

Synopsis

In this book, an integrated introduction to statistical inference is provided from a frequentist likelihood-based viewpoint. Classical results are presented together with recent developments, largely built upon ideas due to R.A. Fisher. The term “neo-Fisherian” highlights this.After a unified review of background material (statistical models, likelihood, data and model reduction, first-order asymptotics) and inference in the presence of nuisance parameters (including pseudo-likelihoods), a self-contained introduction is given to exponential families, exponential dispersion models, generalized linear models, and group families. Finally, basic results of higher-order asymptotics are introduced (index notation, asymptotic expansions for statistics and distributions, and major applications to likelihood inference).The emphasis is more on general concepts and methods than on regularity conditions. Many examples are given for specific statistical models. Each chapter is supplemented with problems and bibliographic notes. This volume can serve as a textbook in intermediate-level undergraduate and postgraduate courses in statistical inference.

Synopsis

In this book, an integrated introduction to the statistical inference is provided from a frequentist likelihood-based viewpoint. Classical results are presented together with recent developments largely built upon ideas due to R.A. Fisher. After a unified review of background material (statistical methods, likelihood, data reductions, first-order asymptotics) and inference in the presence of nuisance parameters (including pseufo-likelihoods), a self-contained introduction is given to exponential families, exponential dispersion models, generalized linear models, and group families. Finally, basic results of higher-order asymptotics are introduced (index notation, asymptotic expansions for statistics and distributions, and major applications to likelihood inference). The emphasis is more on general concepts and methods than on regularity conditions. Many examples are given for specific statistical models. Each chapter is supplemented with exercises, problems and bibliographic notes. This volume can serve as a textbook in intermediate-level undergraduate courses.