Introduction To Mixed Modelling PDF Download
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| Author | : N. W. Galwey |
| Publisher | : John Wiley & Sons |
| Total Pages | : 379 |
| Release | : 2007-04-04 |
| Genre | : Mathematics |
| ISBN | : 047003596X |
Download Introduction to Mixed Modelling Book in PDF, ePub and Kindle
Mixed modelling is one of the most promising and exciting areas ofstatistical analysis, enabling more powerful interpretation of datathrough the recognition of random effects. However, many perceivemixed modelling as an intimidating and specialized technique. Thisbook introduces mixed modelling analysis in a simple andstraightforward way, allowing the reader to apply the techniqueconfidently in a wide range of situations. Introduction to Mixed Modelling shows that mixedmodelling is a natural extension of the more familiar statisticalmethods of regression analysis and analysis of variance. In doingso, it provides the ideal introduction to this importantstatistical technique for those engaged in the statistical analysisof data. This essential book: Demonstrates the power of mixed modelling in a wide range ofdisciplines, including industrial research, social sciences,genetics, clinical research, ecology and agriculturalresearch. Illustrates how the capabilities of regression analysis can becombined with those of ANOVA by the specification of a mixedmodel. 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 widerange of situations and explains how to obtain and interpret BestLinear Unbiased Predictors (BLUPs). Features a supplementary website containing solutions toexercises, further examples, and links to the computer softwaresystems GenStat and R. This book provides a comprehensive introduction to mixedmodelling, ideal for final year undergraduate students,postgraduate students and professional researchers alike. Readerswill come from a wide range of scientific disciplines includingstatistics, biology, bioinformatics, medicine, agriculture,engineering, economics, and social sciences.
| Author | : Walter W. Stroup |
| Publisher | : SAS Institute |
| Total Pages | : 608 |
| Release | : 2018-12-12 |
| Genre | : Computers |
| ISBN | : 163526152X |
Download SAS for Mixed Models Book in PDF, ePub and Kindle
Discover the power of mixed models with SAS. Mixed models—now the mainstream vehicle for analyzing most research data—are part of the core curriculum in most master’s degree programs in statistics and data science. In a single volume, this book updates both SAS® for Linear Models, Fourth Edition, and SAS® for Mixed Models, Second Edition, covering the latest capabilities for a variety of applications featuring the SAS GLIMMIX and MIXED procedures. Written for instructors of statistics, graduate students, scientists, statisticians in business or government, and other decision makers, SAS® for Mixed Models is the perfect entry for those with a background in two-way analysis of variance, regression, and intermediate-level use of SAS. This book expands coverage of mixed models for non-normal data and mixed-model-based precision and power analysis, including the following topics: Random-effect-only and random-coefficients models Multilevel, split-plot, multilocation, and repeated measures models Hierarchical models with nested random effects Analysis of covariance models Generalized linear mixed models This book is part of the SAS Press program.
| Author | : José C. Pinheiro |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 538 |
| Release | : 2009-04-15 |
| Genre | : Computers |
| ISBN | : 1441903178 |
Download Mixed-Effects Models in S and S-PLUS Book in PDF, ePub and Kindle
R, linear models, random, fixed, data, analysis, fit.
| Author | : Eugene Demidenko |
| Publisher | : John Wiley & Sons |
| Total Pages | : 768 |
| Release | : 2013-08-05 |
| Genre | : Mathematics |
| ISBN | : 1118091574 |
Download Mixed Models Book in PDF, ePub and Kindle
Praise for the First Edition “This book will serve to greatly complement the growing number of texts dealing with mixed models, and I highly recommend including it in one’s personal library.” —Journal of the American Statistical Association Mixed modeling is a crucial area of statistics, enabling the analysis of clustered and longitudinal data. Mixed Models: Theory and Applications with R, Second Edition fills a gap in existing literature between mathematical and applied statistical books by presenting a powerful examination of mixed model theory and application with special attention given to the implementation in R. The new edition provides in-depth mathematical coverage of mixed models’ statistical properties and numerical algorithms, as well as nontraditional applications, such as regrowth curves, shapes, and images. The book features the latest topics in statistics including modeling of complex clustered or longitudinal data, modeling data with multiple sources of variation, modeling biological variety and heterogeneity, Healthy Akaike Information Criterion (HAIC), parameter multidimensionality, and statistics of image processing. Mixed Models: Theory and Applications with R, Second Edition features unique applications of mixed model methodology, as well as: Comprehensive theoretical discussions illustrated by examples and figures Over 300 exercises, end-of-section problems, updated data sets, and R subroutines Problems and extended projects requiring simulations in R intended to reinforce material Summaries of major results and general points of discussion at the end of each chapter Open problems in mixed modeling methodology, which can be used as the basis for research or PhD dissertations Ideal for graduate-level courses in mixed statistical modeling, the book is also an excellent reference for professionals in a range of fields, including cancer research, computer science, and engineering.
| Author | : Daniel Spieler |
| Publisher | : Routledge |
| Total Pages | : 292 |
| Release | : 2019-10-28 |
| Genre | : Psychology |
| ISBN | : 1000617467 |
Download New Methods in Cognitive Psychology Book in PDF, ePub and Kindle
This book provides an overview of cutting-edge methods currently being used in cognitive psychology, which are likely to appear with increasing frequency in coming years. Once built around univariate parametric statistics, cognitive psychology courses now seem deficient without some contact with methods for signal processing, spatial statistics, and machine learning. There are also important changes in analyses of behavioral data (e.g., hierarchical modeling and Bayesian inference) and there is the obvious change wrought by the advancement of functional imaging. This book begins by discussing the evidence of this rapid change, for example the movement between using traditional analyses of variance to multi-level mixed models, in psycholinguistics. It then goes on to discuss the methods for analyses of physiological measurements, and how these methods provide insights into cognitive processing. New Methods in Cognitive Psychology provides senior undergraduates, graduates and researchers with cutting-edge overviews of new and emerging topics, and the very latest in theory and research for the more established topics.
| Author | : Walter W. Stroup |
| Publisher | : CRC Press |
| Total Pages | : 558 |
| Release | : 2012-09-24 |
| Genre | : Mathematics |
| ISBN | : 1439815127 |
Download Generalized Linear Mixed Models Book in PDF, ePub and Kindle
Generalized Linear Mixed Models: Modern Concepts, Methods and Applications presents an introduction to linear modeling using the generalized linear mixed model (GLMM) as an overarching conceptual framework. For readers new to linear models, the book helps them see the big picture. It shows how linear models fit with the rest of the core statistics curriculum and points out the major issues that statistical modelers must consider. Along with describing common applications of GLMMs, the text introduces the essential theory and main methodology associated with linear models that accommodate random model effects and non-Gaussian data. Unlike traditional linear model textbooks that focus on normally distributed data, this one adopts a generalized mixed model approach throughout: data for linear modeling need not be normally distributed and effects may be fixed or random. With numerous examples using SAS® PROC GLIMMIX, this book is ideal for graduate students in statistics, statistics professionals seeking to update their knowledge, and researchers new to the generalized linear model thought process. It focuses on data-driven processes and provides context for extending traditional linear model thinking to generalized linear mixed modeling. See Professor Stroup discuss the book.
| Author | : Brady T. West |
| Publisher | : CRC Press |
| Total Pages | : 376 |
| Release | : 2006-11-22 |
| Genre | : Mathematics |
| ISBN | : 1420010433 |
Download Linear Mixed Models Book in PDF, ePub and Kindle
Simplifying the often confusing array of software programs for fitting linear mixed models (LMMs), Linear Mixed Models: A Practical Guide Using Statistical Software provides a basic introduction to primary concepts, notation, software implementation, model interpretation, and visualization of clustered and longitudinal data. This easy-to-nav
| Author | : Andrzej Gałecki |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 558 |
| Release | : 2013-02-05 |
| Genre | : Mathematics |
| ISBN | : 1461439000 |
Download Linear Mixed-Effects Models Using R Book in PDF, ePub and Kindle
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.
| Author | : Eugene Demidenko |
| Publisher | : John Wiley & Sons |
| Total Pages | : 732 |
| Release | : 2005-01-28 |
| Genre | : Mathematics |
| ISBN | : 0471726133 |
Download Mixed Models Book in PDF, ePub and Kindle
A rigorous, self-contained examination of mixed model theory and application Mixed modeling is one of the most promising and exciting areas of statistical analysis, enabling the analysis of nontraditional, clustered data that may come in the form of shapes or images. This book provides in-depth mathematical coverage of mixed models’ statistical properties and numerical algorithms, as well as applications such as the analysis of tumor regrowth, shape, and image. Paying special attention to algorithms and their implementations, the book discusses: Modeling of complex clustered or longitudinal data Modeling data with multiple sources of variation Modeling biological variety and heterogeneity Mixed model as a compromise between the frequentist and Bayesian approaches Mixed model for the penalized log-likelihood Healthy Akaike Information Criterion (HAIC) How to cope with parameter multidimensionality How to solve ill-posed problems including image reconstruction problems Modeling of ensemble shapes and images Statistics of image processing Major results and points of discussion at the end of each chapter along with "Summary Points" sections make this reference not only comprehensive but also highly accessible for professionals and students alike in a broad range of fields such as cancer research, computer science, engineering, and industry.
| Author | : Duchateau, L. |
| Publisher | : ILRI (aka ILCA and ILRAD) |
| Total Pages | : 172 |
| Release | : 1998-01-01 |
| Genre | : |
| ISBN | : 9789291460380 |
Download Linear mixed models. An introduction with applications in veterinary research Book in PDF, ePub and Kindle
