Dirk speelman (44 Ergebnisse)

PAPERBACK. Zustand: VG. English Translation. 219 pages. Book Near Fine. Binding solid, pages crisp and clean. Back bottom edge of cover is partially creased. Some light scuffs and dents. Minimal tip wear at corners.

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Zustand: as new. Amsterdam : P.J. Meertens Instituut, 1999. Paperback. 172 pp. (Publicaties van het Meertens Instituut). Condition : as new copy. ISBN 9789070389604. Keywords : LINGUISTICS.

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Hardcover. Zustand: Brand New. 320 pages. 9.41x6.50x1.06 inches. In Stock.

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Hardcover. Zustand: Brand New. 397 pages. 9.00x6.25x1.00 inches. In Stock.

Taschenbuch. Zustand: Neu. Mixed-Effects Regression Models in Linguistics | Dirk Speelman (u. a.) | Taschenbuch | vii | Englisch | 2019 | Springer | EAN 9783319888507 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

Hardcover. Zustand: Brand New. 320 pages. 9.41x6.50x1.06 inches. In Stock.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - When data consist of grouped observations or clusters, and there is a risk that measurements within the same group are not independent, group-specific random effects can be added to a regression model in order to account for such within-group associations. Regression models that contain such group-specific random effects are called mixed-effects regression models, or simply mixed models. Mixed models are a versatile tool that can handle both balanced and unbalanced datasets and that can also be applied when several layers of grouping are present in the data; these layers can either be nested or crossed. In linguistics, as in many other fields, the use of mixed models has gained ground rapidly over the last decade. This methodological evolution enables us to build more sophisticated and arguably more realistic models, but, due to its technical complexity, also introduces new challenges. This volume brings together a number of promising new evolutions in the use of mixed models in linguistics, but also addresses a number of common complications, misunderstandings, and pitfalls. Topics that are covered include the use of huge datasets, dealing with non-linear relations, issues of cross-validation, and issues of model selection and complex random structures. The volume features examples from various subfields in linguistics. The book also provides R code for a wide range of analyses.…

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - When data consist of grouped observations or clusters, and there is a risk that measurements within the same group are not independent, group-specific random effects can be added to a regression model in order to account for such within-group associations. Regression models that contain such group-specific random effects are called mixed-effects regression models, or simply mixed models. Mixed models are a versatile tool that can handle both balanced and unbalanced datasets and that can also be applied when several layers of grouping are present in the data; these layers can either be nested or crossed. In linguistics, as in many other fields, the use of mixed models has gained ground rapidly over the last decade. This methodological evolution enables us to build more sophisticated and arguably more realistic models, but, due to its technical complexity, also introduces new challenges. This volume brings together a number of promising new evolutions in the use of mixed models in linguistics, but also addresses a number of common complications, misunderstandings, and pitfalls. Topics that are covered include the use of huge datasets, dealing with non-linear relations, issues of cross-validation, and issues of model selection and complex random structures. The volume features examples from various subfields in linguistics. The book also provides R code for a wide range of analyses.…

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Paperback. Zustand: Brand New. 156 pages. 9.25x6.10x0.32 inches. In Stock.