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Mixed-effects models have found broad applications in various fields. As a result, the interest in learning and using these models is rapidly growing.
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1
Robust Mixed Model Analysis
8 May 2019, World Scientific Publishing Co Pte Ltd
Hardcover
in English
- First edition
9814733830 9789814733830
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Book Details
First Sentence
"Mixed effects models, or mixed models, have had a wide-ranging impact inmodern applied statistics."
Table of Contents
Edition Notes
Includes bibliographical references (at 243) and index (at 253).
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Work Description
Mixed-effects models have found broad applications in various fields. As a result, the interest in learning and using these models is rapidly growing. On the other hand, some of these models, such as the linear mixed models and generalized linear mixed models, are highly parametric, involving distributional assumptions that may not be satisfied in real-life problems. Therefore, it is important, from a practical standpoint, that the methods of inference about these models are robust to violation of model assumptions. Fortunately, there is a full scale of methods currently available that are robust in certain aspects. Learning about these methods is essential for the practice of mixed-effects models. This research monograph provides a comprehensive account of methods of mixed model analysis that are robust in various aspects, such as violation of model assumptions, or to outliers. It is also suitable as a reference book for a practitioner who uses the mixed-effects models, a researcher who studies these models, or as a graduate text for a course on mixed-effects models and their applications.
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Feedback?November 26, 2022 | Edited by Kaustubh Chakraborty | Corrected all informations |
August 5, 2020 | Created by ImportBot | import new book |