linear generalized mixed models (245 Ergebnisse)

Hardback. Zustand: Very Good. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.

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Zustand: Good. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In good all round condition. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,750grams, ISBN:9780471193647.

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Taschenbuch. Zustand: Neu. Neuware -This open access book offers an introduction to mixed generalized linear models with applications to the biological sciences, basically approached from an applications perspective, without neglecting the rigor of the theory. For this reason, the theory that supports each of the studied methods is addressed and later - through examples - its application is illustrated. In addition, some of the assumptions and shortcomings of linear statistical models in general are also discussed.An alternative to analyse non-normal distributed response variables is the use of generalized linear models (GLM) to describe the response data with an exponential family distribution that perfectly fits the real response. Extending this idea to models with random effects allows the use of Generalized Linear Mixed Models (GLMMs). The use of these complex models was not computationally feasible until the recent past, when computational advances and improvements to statistical analysis programs allowed users to easily, quickly, and accurately apply GLMM to data sets. GLMMs have attracted considerable attention in recent years. The word 'Generalized' refers to non-normal distributions for the response variable and the word 'Mixed' refers to random effects, in addition to the fixed effects typical of analysis of variance (or regression). With the development of modern statistical packages such as Statistical Analysis System (SAS), R, ASReml, among others, a wide variety of statistical analyzes are available to a wider audience. However, to be able to handle and master more sophisticated models requires proper training and great responsibility on the part of the practitioner to understand how these advanced tools work. GMLM is an analysis methodology used in agriculture and biology that can accommodate complex correlation structures and types of response variables.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 444 pp. Englisch.

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N.A. Zustand: New. ISBN:9780367221409.

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Zustand: Fine. Second Edition. Hardcover : pp. [iv] xxv [xxvi] 384 [408] : blue and white boards. Wiley Series in Probability and Statistics.

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hardcover. Zustand: Good. 2nd Edition. Ships in a BOX from Central Missouri! May not include working access code. Will not include dust jacket. Has used sticker(s) and some writing or highlighting. UPS shipping for most packages, (Priority Mail for AK/HI/APO/PO Boxes).

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Taschenbuch. Zustand: Neu. Neuware -Now in its second edition, this book covers two major classes of mixed effects models¿linear mixed models and generalized linear mixed models¿and it presents an up-to-date account of theory and methods in analysis of these models as well as their applications in various fields. It offers a systematic approach to inference about non-Gaussian linear mixed models. Furthermore, it discusses the latest developments and methods in the field, incorporating relevant updates since publication of the first edition. These include advances in high-dimensional linear mixed models in genome-wide association studies (GWAS), advances in inference about generalized linear mixed models with crossed random effects, new methods in mixed model prediction, mixed model selection, and mixed model diagnostics.This book is suitable for students, researchers, and practitioners who are interested in using mixed models for statistical data analysis with public health applications. It is best for graduate courses in statistics, or for those who have taken a first course in mathematical statistics, are familiar with using computers for data analysis, and have a foundational background in calculus and linear algebra.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 360 pp. Englisch.

Hardcover. Zustand: gut. 2013. The aim of this book is an applied and unified introduction into parametric, non- and semiparametric regression that closes the gap between theory and application. The most important models and methods in regression are presented on a solid formal basis, and their appropriate application is shown through many real data examples and case studies. Availability of (user-friendly) software has been a major criterion for the methods selected and presented. Thus, the book primarily targets an audience that includes students, teachers and practitioners in social, economic, and life sciences, as well as students and teachers in statistics programs, and mathematicians and computer scientists with interests in statistical modeling and data analysis. It is written on an intermediate mathematical level and assumes only knowledge of basic probability, calculus, and statistics. The most important definitions and statements are concisely summarized in boxes. Two appendices describe required matrix algebra, as well as elements of probability calculus and statistical inference. Autor: Ludwig Fahrmeir is Professor emeritus at the Department of Statistics at Ludwig-Maximilians-University Munich. From 1995 to 2006 he was speaker of the Collaborative Research Center 'Statistical Analysis of Discrete Data', supported financially by the German National Science Foundation. His main research interests are semiparametric regression, longitudinal data analysis and spatial statistics, with applications ranging from social science and risk management to public health and neuroscience. - Thomas Kneib is Professor for Statistics at Georg August University Göttingen, Germany, where he is speaker of the interdisciplinary Centre for Statistics and a Research Training Group on "Scaling Problems in Statistics". He received his PhD in Statistics at Ludwig-Maximilians-University Munich and, during his PostDoc phase, has been Visiting Professor for Applied Statistics at the University of Ulm and Substitute Professor for Statistics at Georg-August-University Göttingen. From 2009 until 2011 he has been Professor for Applied Statistics at Carl von Ossietzky University Oldenburg. His main research interests include semiparametric regression, spatial statistics and quantile regression. - Stefan Lang is Professor for Applied Statistics at University of Innsbruck, Austria. He received his PhD at Ludwig-Maximilians-University Munich. From 2005 to 2006 he has been Professor for Statistics at University of Leipzig. He is currently editor of Advances of Statistical Analysis and Associate Editor of Statistical Modelling. His main research interests include semiparametric and spatial regression, multilevel modelling and complex Bayesian models, with applications among others in environmetrics, marketing science, real estate and actuarial science. - Brian D. Marx is a full professor in the Department of Experimental Statisitics at Louisiana State University. His main research interests include P-spline smoothiing, ill-conditioned regression problems, and high-dimensional chemometric applications. He is currently serving as coordinating editor for the journal Statistical Modelling and is past chair of the Statistical Modelling Society. Content: Introduction.- Regression Models.- The Classical Linear Model.- Extensions of the Classical Linear Model.- Generalized Linear Models.- Categorical Regression Models.- Mixed Models.- Nonparametric Regression.- Structured Additive Regression.- Quantile Regression.- A Matrix Algebra.- B Probability Calculus and Statistical Inference.- Bibliography.- Index. Zusatzinfo XIV, 698 p. Verlagsort Berlin Sprache englisch Maße 155 x 235 mm Mathematik / Informatik Mathematik Wirtschaft Lexika Generalized Linear Models linear regression mixed models Regression Statistik Semiparametric Regression spatial regression Wirtschaftsstatistik ISBN-10 3-642-34332-5 / 3642343325 ISBN-13 978-3-642-34332-2 / 9783642343322 In englischer Sprache. 650 pages. 15,6 x 3,8 x 23,4 cm.

Hardcover. Zustand: New. New. book.

Paperback. Zustand: Brand New. 271 pages. 9.00x6.00x0.63 inches. In Stock.

Hardcover. Zustand: As New. later printing. As new. Book.

Hardcover. Zustand: As New. later printing. As new. Book.

Hardcover. Zustand: New. ISBN:9781439815120.

Hardcover. Zustand: New. ISBN:9780387479415.

paperback. Zustand: New. Paperback. Pub Date: 2013-03-01 Pages: 165 Language: Chinese Publisher: Science Press. LLC Linear and generalized linear mixed models and statistical diagnostics system introduces the basic theory of linear mixed models and generalized linear mixed models and method. It includes two models parameter estimation. hypothesis testing. confidence regions and statistical diagnose problems. Statistical analysis is focused on the diagnosis of the two models. using probe data deletion problem studied .