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eBook Principles of Statistical Inference ePub

eBook Principles of Statistical Inference ePub

by Alessandra Salvan,Luigi Pace

  • ISBN: 9810230664
  • Category: Mathematics
  • Subcategory: Math Science
  • Author: Alessandra Salvan,Luigi Pace
  • Language: English
  • Publisher: World Scientific Pub Co Inc; 1st edition (January 15, 1997)
  • Pages: 535
  • ePub book: 1720 kb
  • Fb2 book: 1767 kb
  • Other: lrf txt mobi lit
  • Rating: 4.9
  • Votes: 352

Description

Principles of statistical inference from a neo-Fisherian perspective. Best conditional tests for separate families of hypotheses. Journal of the Royal Statistical Society: Series B (Methodological) 52 (. 1990.

Principles of statistical inference from a neo-Fisherian perspective. World Scientific Publishing Company, 1997. Introduzione alla statistica: Inferenza, verosimiglianza, modelli. xvi, 422 p. L Pace, A Salvan. Adjusting composite likelihood ratio statistics. L Pace, A Salvan, N Sartori. Statistica Sinica, 129-148, 2011. Adjustments of the profile likelihood from a new perspective.

View the profiles of people named Alessandra Salvan. People named Alessandra Salvan.

Alessandra Salvan of University of Padova, Padova (UNIPD) Read 40. .Principles of statistical inference from a neo-Fisherian perspective.

Alessandra Salvan of University of Padova, Padova (UNIPD) Read 40 publications Contact Alessandra Salvan. For inference in complex models, composite likelihood combines genuine likelihoods based on lowdimensional portions of the data, with weights to be chosen.

Principles of statistical inference : from a neo-Fisherian perspective. Luigi Pace, Alessandra Salvan. Composite likelihood may be useful for approximating likelihood based inference when the full likelihood is too complex to deal with. 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, largel. More). Stemming from a misspecified model, inference based on composit.

Principles Of Statistical Inference book. Principles Of Statistical Inference: From A Neo Fisherian Perspective (Advanced Series On Statistical Science & Applied Probability). by. Classical results are presented together with recent developments, largely built upon ideas due to .

Pace, L. and Salvan, A. (1997). Principles of Statistical Inference from a Neo-Fisherian Perspective, Advanced Series on Statistical Science and Applied Probability, Vol. 4, World Scientific, Singapore. zbMATHGoogle Scholar. Sartori, . Salvan, A. and Pace, L. (2003). A note on directed adjusted profile likelihoods,Journal of Statistical Planning and Inference,110, 1–. ATHGoogle Scholar. Likelihood Methods in Statistics, Oxford University Press, Oxford.

Cambridge Core - Statistical Theory and Methods - Essentials of Statistical Inference - by G. A. Young. Pace, Luigi Salvan, Alessandra and Ventura, Laura 2011. Adjustments of profile likelihood through predictive densities

Cambridge Core - Statistical Theory and Methods - Essentials of Statistical Inference - by G. Adjustments of profile likelihood through predictive densities. Annals of the Institute of Statistical Mathematics, Vol. 63, Issue.

Throughout, the text concentrates on concepts, rather than mathematical detail, while maintaining appropriate levels of formality. Some prior knowledge of probability is assumed, while some previous knowledge of the objectives and main approaches to statistical inference would be helpful but is not essential.

eText ISBN: 9789813103016, 9813103019.

Essential statistical inference by Boos and Stefanski is an excellent book with appeal to advanced undergraduate and .

An appropriate list of references is given at the end of the book. My colleagues and I have taught from this textbook or earlier iterations for the past six years and students consistently gave the text high marks for its clarity, instructive examples and end-of-chapter exercises.

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.