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Introduction To Mixed Modelling

Introduction to Mixed Modelling PDF
Author: N. W. Galwey
Publisher: John Wiley & Sons
Release: 2014-08-26
ISBN: 1118861825
Size: 17.74 MB
Format: PDF, Docs
Category : Mathematics
Languages : en
Pages : 504
View: 1431

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Introduction To Mixed Modelling

by N. W. Galwey, Introduction To Mixed Modelling Books available in PDF, EPUB, Mobi Format. Download Introduction To Mixed Modelling books, Mixed modelling is very useful, and easier than youthink! Mixed modelling is now well established as a powerful approachto statistical data analysis. It is based on the recognition ofrandom-effect terms in statistical models, leading to inferencesand estimates that have much wider applicability and are morerealistic than those otherwise obtained. Introduction to Mixed Modelling leads the readerinto mixed modelling as a natural extension of two more familiarmethods, regression analysis and analysis of variance. It providespractical guidance combined with a clear explanation of theunderlying concepts. Like the first edition, this new edition shows diverseapplications of mixed models, provides guidance on theidentification of random-effect terms, and explains how to obtainand interpret best linear unbiased predictors (BLUPs). It also introduces several important new topics, including thefollowing: ulstyle="line-height: 25px; margin-left: 15px; margin-top: 0px; font-family: Arial; font-size: 13px;" Use of the software SAS, in addition to GenStat and R. Meta-analysis and the multiple testing problem. The Bayesian interpretation of mixed models. Including numerous practical exercises with solutions, this bookprovides an ideal introduction to mixed modelling for final yearundergraduate students, postgraduate students and professionalresearchers. It will appeal to readers from a wide range ofscientific disciplines including statistics, biology,bioinformatics, medicine, agriculture, engineering, economics,archaeology and geography. Praise for the first edition: “One of the main strengths of the text is the bridge itprovides between traditional analysis of variance and regressionmodels and the more recently developed class of mixed models...Eachchapter is well-motivated by at least one carefully chosenexample...demonstrating the broad applicability of mixed models inmany different disciplines...most readers will likely learnsomething new, and those previously unfamiliar with mixed modelswill obtain a solid foundation on thistopic.”—Kerrie Nelson University ofSouth Carolina, in American Statistician, 2007



Introduction To Mixed Modelling

Introduction to Mixed Modelling PDF
Author: N. W. Galwey
Publisher: John Wiley & Sons
Release: 2007-04-04
ISBN: 047003596X
Size: 77.21 MB
Format: PDF
Category : Mathematics
Languages : en
Pages : 376
View: 5626

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Introduction To Mixed Modelling

by N. W. Galwey, Introduction To Mixed Modelling Books available in PDF, EPUB, Mobi Format. Download Introduction To Mixed Modelling books, Mixed modelling is one of the most promising and exciting areas of statistical analysis, enabling more powerful interpretation of data through the recognition of random effects. However, many perceive mixed modelling as an intimidating and specialized technique. This book introduces mixed modelling analysis in a simple and straightforward way, allowing the reader to apply the technique confidently in a wide range of situations. Introduction to Mixed Modelling shows that mixed modelling is a natural extension of the more familiar statistical methods of regression analysis and analysis of variance. In doing so, it provides the ideal introduction to this important statistical technique for those engaged in the statistical analysis of data. This essential book: Demonstrates the power of mixed modelling in a wide range of disciplines, including industrial research, social sciences, genetics, clinical research, ecology and agricultural research. Illustrates how the capabilities of regression analysis can be combined with those of ANOVA by the specification of a mixed model. Introduces the criterion of Restricted Maximum Likelihood (REML) for the fitting of a mixed model to data. Presents the application of mixed model analysis to a wide range of situations and explains how to obtain and interpret Best Linear Unbiased Predictors (BLUPs). Features a supplementary website containing solutions to exercises, further examples, and links to the computer software systems GenStat and R. This book provides a comprehensive introduction to mixed modelling, ideal for final year undergraduate students, postgraduate students and professional researchers alike. Readers will come from a wide range of scientific disciplines including statistics, biology, bioinformatics, medicine, agriculture, engineering, economics, and social sciences.



Mixed Effects Models And Extensions In Ecology With R

Mixed Effects Models and Extensions in Ecology with R PDF
Author: Alain Zuur
Publisher: Springer Science & Business Media
Release: 2009-03-05
ISBN: 9780387874586
Size: 42.75 MB
Format: PDF, ePub, Mobi
Category : Science
Languages : en
Pages : 574
View: 2287

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Mixed Effects Models And Extensions In Ecology With R

by Alain Zuur, Mixed Effects Models And Extensions In Ecology With R Books available in PDF, EPUB, Mobi Format. Download Mixed Effects Models And Extensions In Ecology With R books, This book discusses advanced statistical methods that can be used to analyse ecological data. Most environmental collected data are measured repeatedly over time, or space and this requires the use of GLMM or GAMM methods. The book starts by revising regression, additive modelling, GAM and GLM, and then discusses dealing with spatial or temporal dependencies and nested data.



Introduction To Population Pharmacokinetic Pharmacodynamic Analysis With Nonlinear Mixed Effects Models

Introduction to Population Pharmacokinetic   Pharmacodynamic Analysis with Nonlinear Mixed Effects Models PDF
Author: Joel S. Owen
Publisher: John Wiley & Sons
Release: 2014-07-08
ISBN: 9780470582299
Size: 30.30 MB
Format: PDF, Kindle
Category : Mathematics
Languages : en
Pages : 320
View: 2607

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Introduction To Population Pharmacokinetic Pharmacodynamic Analysis With Nonlinear Mixed Effects Models

by Joel S. Owen, Introduction To Population Pharmacokinetic Pharmacodynamic Analysis With Nonlinear Mixed Effects Models Books available in PDF, EPUB, Mobi Format. Download Introduction To Population Pharmacokinetic Pharmacodynamic Analysis With Nonlinear Mixed Effects Models books, "This useful guide helps pharmaceutical scientists and students learn the requisite information needed to perform mixed effect modeling of pharmacologic data using the NONMEM software package"--Provided by publisher.



Analyzing Ecological Data

Analyzing Ecological Data PDF
Author: Alain Zuur
Publisher: Springer
Release: 2007-08-29
ISBN: 0387459723
Size: 61.78 MB
Format: PDF, ePub
Category : Science
Languages : en
Pages : 672
View: 2404

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Analyzing Ecological Data

by Alain Zuur, Analyzing Ecological Data Books available in PDF, EPUB, Mobi Format. Download Analyzing Ecological Data books, This book provides a practical introduction to analyzing ecological data using real data sets. The first part gives a largely non-mathematical introduction to data exploration, univariate methods (including GAM and mixed modeling techniques), multivariate analysis, time series analysis, and spatial statistics. The second part provides 17 case studies. The case studies include topics ranging from terrestrial ecology to marine biology and can be used as a template for a reader’s own data analysis. Data from all case studies are available from www.highstat.com. Guidance on software is provided in the book.



Introduction To Stochastic Differential Equations With Applications To Modelling In Biology And Finance

Introduction to Stochastic Differential Equations with Applications to Modelling in Biology and Finance PDF
Author: Carlos A. Braumann
Publisher: Wiley
Release: 2019-04-29
ISBN: 1119166063
Size: 46.70 MB
Format: PDF, ePub, Mobi
Category : Mathematics
Languages : en
Pages : 304
View: 3943

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Introduction To Stochastic Differential Equations With Applications To Modelling In Biology And Finance

by Carlos A. Braumann, Introduction To Stochastic Differential Equations With Applications To Modelling In Biology And Finance Books available in PDF, EPUB, Mobi Format. Download Introduction To Stochastic Differential Equations With Applications To Modelling In Biology And Finance books, A comprehensive introduction to the core issues of stochastic differential equations and their effective application Introduction to Stochastic Differential Equations with Applications to Modelling in Biology and Finance offers a comprehensive examination to the most important issues of stochastic differential equations and their applications. The author — a noted expert in the field — includes myriad illustrative examples in modelling dynamical phenomena subject to randomness, mainly in biology, bioeconomics and finance, that clearly demonstrate the usefulness of stochastic differential equations in these and many other areas of science and technology. The text also features real-life situations with experimental data, thus covering topics such as Monte Carlo simulation and statistical issues of estimation, model choice and prediction. The book includes the basic theory of option pricing and its effective application using real-life. The important issue of which stochastic calculus, Itô or Stratonovich, should be used in applications is dealt with and the associated controversy resolved. Written to be accessible for both mathematically advanced readers and those with a basic understanding, the text offers a wealth of exercises and examples of application. This important volume: Contains a complete introduction to the basic issues of stochastic differential equations and their effective application Includes many examples in modelling, mainly from the biology and finance fields Shows how to: Translate the physical dynamical phenomenon to mathematical models and back, apply with real data, use the models to study different scenarios and understand the effect of human interventions Conveys the intuition behind the theoretical concepts Presents exercises that are designed to enhance understanding Offers a supporting website that features solutions to exercises and R code for algorithm implementation Written for use by graduate students, from the areas of application or from mathematics and statistics, as well as academics and professionals wishing to study or to apply these models, Introduction to Stochastic Differential Equations with Applications to Modelling in Biology and Finance is the authoritative guide to understanding the issues of stochastic differential equations and their application.



Introduction To Modern Modelling Methods

Introduction to Modern Modelling Methods PDF
Author: D. Betsy McCoach
Publisher: SAGE
Release: 2022-03-26
ISBN: 152971110X
Size: 61.35 MB
Format: PDF, Kindle
Category : Reference
Languages : en
Pages : 304
View: 3792

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Introduction To Modern Modelling Methods

by D. Betsy McCoach, Introduction To Modern Modelling Methods Books available in PDF, EPUB, Mobi Format. Download Introduction To Modern Modelling Methods books, Using concise and direct language, Betsy McCoach′s book imparts a wide range of modeling techniques for use with quantitative data, including: From 2-level multilevel models to longitudinal modeling using multilevel and stuctural equation modeling (SEM) techniques. Part of The SAGE Quantitative Research Kit, this book offers the know-how and confidence needed to succeed on your quantitative research journey.



Introduction To Graphical Modelling

Introduction to Graphical Modelling PDF
Author: David Edwards
Publisher: Springer Science & Business Media
Release: 2000-06-15
ISBN: 9780387950549
Size: 48.24 MB
Format: PDF
Category : Computers
Languages : en
Pages : 333
View: 773

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Introduction To Graphical Modelling

by David Edwards, Introduction To Graphical Modelling Books available in PDF, EPUB, Mobi Format. Download Introduction To Graphical Modelling books, A useful introduction to this topic for both students and researchers, with an emphasis on applications and practicalities rather than on a formal development. It is based on the popular software package for graphical modelling, MIM, freely available for downloading from the Internet. Following a description of some of the basic ideas of graphical modelling, subsequent chapters describe particular families of models, including log-linear models, Gaussian models, and models for mixed discrete and continuous variables. Further chapters cover hypothesis testing and model selection. Chapters 7 and 8 are new to this second edition and describe the use of directed, chain, and other graphs, complete with a summary of recent work on causal inference.



Linear Mixed Effects Models Using R

Linear Mixed Effects Models Using R PDF
Author: Andrzej Gałecki
Publisher: Springer Science & Business Media
Release: 2013-02-05
ISBN: 1461439000
Size: 71.98 MB
Format: PDF, ePub, Mobi
Category : Mathematics
Languages : en
Pages : 542
View: 5119

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Linear Mixed Effects Models Using R

by Andrzej Gałecki, Linear Mixed Effects Models Using R Books available in PDF, EPUB, Mobi Format. Download Linear Mixed Effects Models Using R books, Linear mixed-effects models (LMMs) are an important class of statistical models that can be used to analyze correlated data. Such data are encountered in a variety of fields including biostatistics, public health, psychometrics, educational measurement, and sociology. This book aims to support a wide range of uses for the models by applied researchers in those and other fields by providing state-of-the-art descriptions of the implementation of LMMs in R. To help readers to get familiar with the features of the models and the details of carrying them out in R, the book includes a review of the most important theoretical concepts of the models. The presentation connects theory, software and applications. It is built up incrementally, starting with a summary of the concepts underlying simpler classes of linear models like the classical regression model, and carrying them forward to LMMs. A similar step-by-step approach is used to describe the R tools for LMMs. All the classes of linear models presented in the book are illustrated using real-life data. The book also introduces several novel R tools for LMMs, including new class of variance-covariance structure for random-effects, methods for influence diagnostics and for power calculations. They are included into an R package that should assist the readers in applying these and other methods presented in this text.