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Robust mixed model analysis

Jiang, Jiming.

Robust mixed model analysis

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Robust mixed model analysisJiming Jiang.
作者:	Jiang, Jiming.
出版者:	Singapore :World Scientific Publishing,c2019.
面頁冊數:	1 online resource (268 p.) :ill.
標題:	Multilevel models (Statistics)
電子資源:	https://www.worldscientific.com/worldscibooks/10.1142/9888#t=toc
ISBN:	9789814733847$q(electronic bk.)

Robust mixed model analysis
Jiang, Jiming.

Robust mixed model analysis[electronic resource] /Jiming Jiang. - 1st ed. - Singapore :World Scientific Publishing,c2019. - 1 online resource (268 p.) :ill.

Includes bibliographical references (p. 243-252) and index.

"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 to violation of model assumptions, or to outliers. It is suitable as a reference book for a practitioner who uses the mixed-effects models, and a researcher who studies these models. It can also be treated as a graduate text for a course on mixed-effects models and their applications."--

ISBN: 9789814733847$q(electronic bk.)Subjects--Topical Terms:

240063
Multilevel models (Statistics)

LC Class. No.: QA278 / .J53 2019

Dewey Class. No.: 519.5/36

Robust mixed model analysis
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260 $a Singapore : $b World Scientific Publishing, $c c2019.
300 $a 1 online resource (268 p.) : $b ill.
504 $a Includes bibliographical references (p. 243-252) and index.
520 $a "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 to violation of model assumptions, or to outliers. It is suitable as a reference book for a practitioner who uses the mixed-effects models, and a researcher who studies these models. It can also be treated as a graduate text for a course on mixed-effects models and their applications."-- $c Publisher's website.
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