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Robust mixed model analysis[electron...

Jiang, Jiming.

Robust mixed model analysis[electronic resource] /

レコード種別:	コンピュータ・メディア : 単行資料
[NT 15000414] null:	519.5/36
タイトル / 著者:	Robust mixed model analysis/ Jiming Jiang.
著者:	Jiang, Jiming.
出版された:	Singapore : : World Scientific Publishing,, c2019.
記述:	1 online resource (268 p.) : : ill.
主題:	Multilevel models (Statistics)
主題:	Linear models (Statistics)
主題:	Mathematical models
国際標準図書番号 (ISBN) :	9789814733847
[NT 15000227] null:	Includes bibliographical references (p. 243-252) and index.
[NT 15000229] null:	"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."--
電子資源:	https://www.worldscientific.com/worldscibooks/10.1142/9888#t=toc

Robust mixed model analysis[electronic resource] /
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: 9789814733847Subjects--Topical Terms:

340208
Multilevel models (Statistics)

LC Class. No.: QA278 / .J53 2019

Dewey Class. No.: 519.5/36

Robust mixed model analysis[electronic resource] /
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245 1 0 $a Robust mixed model analysis $h [electronic resource] / $c Jiming Jiang.
250 $a 1st ed.
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.
588 $a Title from web page (viewed April 10, 2019)
650 0 $a Multilevel models (Statistics) $3 340208
650 0 $a Linear models (Statistics) $3 224081
650 0 $a Mathematical models $3 135201
856 4 0 $u https://www.worldscientific.com/worldscibooks/10.1142/9888#t=toc

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