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Aggiungi al carrelloPaperback. Condizione: Very Good. Random Effect and Latent Variable Model Selection: 192 (Lecture Notes in Statistics) This book is in very good condition and will be shipped within 24 hours of ordering. The cover may have some limited signs of wear but the pages are clean, intact and the spine remains undamaged. This book has clearly been well maintained and looked after thus far. Money back guarantee if you are not satisfied. See all our books here, order more than 1 book and get discounted shipping. .
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ISBN 10: 1138627399 ISBN 13: 9781138627390
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Editore: Bloomsbury Publishing Plc, US, 1980
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Aggiungi al carrelloHardback. Condizione: New. The author focuses on two parallel folk trends: the Freedom Songs arising from the civil rights battles in the South and the topical songs composed by Northern writer/singers.
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Da: AHA-BUCH GmbH, Einbeck, Germania
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - Random Effect and Latent Variable Model Selection In recent years, there has been a dramatic increase in the collection of multivariate and correlated data in a wide variety of elds. For example, it is now standard pr- tice to routinely collect many response variables on each individual in a study. The different variables may correspond to repeated measurements over time, to a battery of surrogates for one or more latent traits, or to multiple types of outcomes having an unknown dependence structure. Hierarchical models that incorporate subje- speci c parameters are one of the most widely-used tools for analyzing multivariate and correlated data. Such subject-speci c parameters are commonly referred to as random effects, latent variables or frailties. There are two modeling frameworks that have been particularly widely used as hierarchical generalizations of linear regression models. The rst is the linear mixed effects model (Laird and Ware , 1982) and the second is the structural equation model (Bollen , 1989). Linear mixed effects (LME) models extend linear regr- sion to incorporate two components, with the rst corresponding to xed effects describing the impact of predictors on the mean and the second to random effects characterizing the impact on the covariance. LMEs have also been increasingly used for function estimation. In implementing LME analyses, model selection problems are unavoidable. For example, there may be interest in comparing models with and without a predictor in the xed and/or random effects component.
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Random Effect and Latent Variable Model Selection | David Dunson | Taschenbuch | x | Englisch | 2008 | Springer Nature B.V. | EAN 9780387767208 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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