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Statistical Inference Based on the likelihood (Monographs on Statistics and Applied Probability)

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Published by Chapman & Hall/CRC .
Written in English


Book details:

The Physical Object
Number of Pages352
ID Numbers
Open LibraryOL7478977M
ISBN 10041260650X
ISBN 109780412606502

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Statistical inference is often based on the notion of likelihood (cf. Azzalini ()) and the Maximum Likelihood principle plays the fundamental role in the process. In the present note we derive. The Likelihood plays a key role in both introducing general notions of statistical theory, and in developing specific methods. This book introduces likelihood-based statistical theory and related methods from a classical viewpoint, and demonstrates how the main body of currently used statistical techniques can be generated from a few key concepts, in particular the ng on those.   Mathematical Statistics: An Introduction to Likelihood Based Inference makes advanced topics accessible and understandable and covers many topics in more depth than typical mathematical statistics textbooks. It includes numerous examples, case studies, a large number of exercises ranging from drill and skill to extremely difficult problems, and Author: Richard J. Rossi.   The Likelihood plays a key role in both introducing general notions of statistical theory, and in developing specific methods. This book introduces likelihood-based statistical theory and related methods from a classical viewpoint, and demonstrates how the main body of currently used statistical techniques can be generated from a few key concepts, in particular the by:

Statistical Inference Based on the likelihood: Based on the Likeihood (Chapman & Hall/CRC Monographs on Statistics and Applied Probability Book 68) - Kindle edition by Azzalini, Adelchi. Download it once and read it on your Kindle device, PC, phones or tablets. Use features like bookmarks, note taking and highlighting while reading Statistical Inference Based on the likelihood: Based on the 3/5(1). 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 by: Starting from the simplest considerations and assuming no more than a basic acquaintance with probability theory, the author sets out to reconstruct a consistent theory of statistical inference in science. Using the likelihood approach, he explores estimation, tests of significance, randomization, experimental design, and other statistical by: This book covers modern statistical inference based on likelihood with applications in medicine, epidemiology and biology. Two introductory chapters discuss the importance of statistical models in applied quantitative research and the central role of the likelihood function. The .

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