Linear Mixed-Effects Models Using R

Linear models courses are often presented as either theoretical or applied. Consequently, students may find themselves either proving theorems or using high-level procedures like PROC GLM to analyze d

Reveals How HMMs Can Be Used as General-Purpose Time Series ModelsImplements all methods in RHidden Markov Models for Time Series: An Introduction Using R applies hidden Markov models (HMMs) to a wide

An insightful approach to the analysis of variance in the study of linear models Linear Models explores the theory of linear models and the dynamic relationships that these models have with Analysis

Generalized Linear Models and Extensions, Second Edition provides a comprehensive overview of the nature and scope of generalized linear models (GLMs) and of the major changes to the basic GLM algorit

A precise and accessible presentation of linear model theory, illustrated with data examplesStatisticians often use linear models for data analysis and for developing new statistical methods. Most boo

Statistical analysis is ubiquitous in modern medical research. Logistic regression, generalized linear models, random effects models, and Cox's regression all have become commonplace in the medical li

This textbook for students in the health and social sciences covers the basics of linear model methods with a minimum of mathematics, assuming only a pre-calculus background. Numerous examples drawn from the news and current events with an emphasis on health issues, illustrate the concepts in an immediately accessible way. Methods covered include linear regression models, Poisson regression, logistic regression, proportional hazards regression, survival analysis, and nonparametric regression. The author emphasizes interpretation of computer output in terms of the motivating example. All of the R code is provided and carefully explained, allowing readers to quickly apply the methods to their own data. Plenty of exercises help students think about the issues involved in the analysis and its interpretation. Code and datasets are available for download from the book's website at www.cambridge.org/zelterman

1. Applied Linear Statistical Models contains 14 chapters on the Applied Linear Regression Models, plus an additional 16 chapters on single and multifactor ANOVA and Experimental Design. The standard

Advances in training models with log-linear structures, with topics including variable selection, the geometry of neural nets, and applications.Log-linear models play a key role in modern big data and

Examine business problems and use a practical analytical approach to solve them by implementing predictive models and machine learning techniques using SAS and the R analytical language. This bo

Linear Models and the Relevant Distributions and Matrix Algebra provides in-depth and detailed coverage of the use of linear statistical models as a basis for parametric and predictive inference. It c

"This book presents an overview of the foundations and the key ideas and results of linear and generalized linear models under one cover"--

A fully updated edition of this key text on mixed models, focusing on applications in medical research The application of mixed models is an increasingly popular way of analysing medical data, particu

Produce a variety of effects using paint, found images, wax, cardboard, wire, leaves, newspaper, stencils, stamps, and moreInspiration and step-by-step-instructions for 23 mixed-media projectsCreate i

This textbook provides a wide-ranging introduction to the use and theory of linear models for analyzing data. The author's emphasis is on providing a unified treatment of linear models, including anal

This textbook provides a wide-ranging introduction to the use and theory of linear models for analyzing data. The author's emphasis is on providing a unified treatment of linear models, including anal

This textbook for a second course in basic statistics for undergraduates or first-year graduate students introduces linear regression models and describes other linear models including Poisson regression, logistic regression, proportional hazards regression, and nonparametric regression. Numerous examples drawn from the news and current events with an emphasis on health issues illustrate these concepts. Assuming only a pre-calculus background, the author keeps equations to a minimum and demonstrates all computations using SAS. Most of the programs and output are displayed in a self-contained way, with an emphasis on the interpretation of the output in terms of how it relates to the motivating example. Plenty of exercises conclude every chapter. All of the datasets and SAS programs are available from the book's website, along with other ancillary material.

This set features:Variance Components by Shayle R. Searle, George Casella, Charles E. McCulloch (978-0-470-00959-8)Matrix Algebra for Useful Statistics by Shayle R. Searle (978-0-470-00961-1)Linear Mo

This set contains: 9780471221746 SAS for Linear Models, Fourth Edition by Ramon Littell, Walter W. Stroup, Rudolf Freund and 9780471754985 Linear Models in Statistics, Second Edition by Alvin C. Rench

Data Analysis Using Regression and Multilevel/Hierarchical Models, first published in 2007, is a comprehensive manual for the applied researcher who wants to perform data analysis using linear and nonlinear regression and multilevel models. The book introduces a wide variety of models, whilst at the same time instructing the reader in how to fit these models using available software packages. The book illustrates the concepts by working through scores of real data examples that have arisen from the authors' own applied research, with programming codes provided for each one. Topics covered include causal inference, including regression, poststratification, matching, regression discontinuity, and instrumental variables, as well as multilevel logistic regression and missing-data imputation. Practical tips regarding building, fitting, and understanding are provided throughout.

Data Analysis Using Regression and Multilevel/Hierarchical Models, first published in 2007, is a comprehensive manual for the applied researcher who wants to perform data analysis using linear and nonlinear regression and multilevel models. The book introduces a wide variety of models, whilst at the same time instructing the reader in how to fit these models using available software packages. The book illustrates the concepts by working through scores of real data examples that have arisen from the authors' own applied research, with programming codes provided for each one. Topics covered include causal inference, including regression, poststratification, matching, regression discontinuity, and instrumental variables, as well as multilevel logistic regression and missing-data imputation. Practical tips regarding building, fitting, and understanding are provided throughout.

Linear algebra and the study of matrix algorithms have become fundamental to the development of statistical models. Using a vector-space approach, this book provides an understanding of the major conc

This is a practical guide to P-splines, a simple, flexible and powerful tool for smoothing. P-splines combine regression on B-splines with simple, discrete, roughness penalties. They were introduced by the authors in 1996 and have been used in many diverse applications. The regression basis makes it straightforward to handle non-normal data, like in generalized linear models. The authors demonstrate optimal smoothing, using mixed model technology and Bayesian estimation, in addition to classical tools like cross-validation and AIC, covering theory and applications with code in R. Going far beyond simple smoothing, they also show how to use P-splines for regression on signals, varying-coefficient models, quantile and expectile smoothing, and composite links for grouped data. Penalties are the crucial elements of P-splines; with proper modifications they can handle periodic and circular data as well as shape constraints. Combining penalties with tensor products of B-splines extends these

This volume deals with first- and second-order complex equations of hyperbolic and mixed types. The authors investigate in detail general boundary value problems for linear and quasilinear complex equ

The material covered by this book consists of a collection of regression models beyond linear regression. The linear regression model is presented in the first chapter in order to refresh the reader’s

This book provides a handy, unified introduction to the theory of compact extended formulations of exponential-size integer linear programming (ILP) models. Compact extended formulations are equally p

Quantitative Methods in Archaeology Using R is the first hands-on guide to using the R statistical computing system written specifically for archaeologists. It shows how to use the system to analyze many types of archaeological data. Part I includes tutorials on R, with applications to real archaeological data showing how to compute descriptive statistics, create tables, and produce a wide variety of charts and graphs. Part II addresses the major multivariate approaches used by archaeologists, including multiple regression (and the generalized linear model); multiple analysis of variance and discriminant analysis; principal components analysis; correspondence analysis; distances and scaling; and cluster analysis. Part III covers specialized topics in archaeology, including intra-site spatial analysis, seriation, and assemblage diversity.

Quantitative Methods in Archaeology Using R is the first hands-on guide to using the R statistical computing system written specifically for archaeologists. It shows how to use the system to analyze many types of archaeological data. Part I includes tutorials on R, with applications to real archaeological data showing how to compute descriptive statistics, create tables, and produce a wide variety of charts and graphs. Part II addresses the major multivariate approaches used by archaeologists, including multiple regression (and the generalized linear model); multiple analysis of variance and discriminant analysis; principal components analysis; correspondence analysis; distances and scaling; and cluster analysis. Part III covers specialized topics in archaeology, including intra-site spatial analysis, seriation, and assemblage diversity.

Using the open source R language, you can build powerful statistical models to answer many of your most challenging questions. R has traditionally been difficult for non-statisticians to learn, and mo

This book presents a greatly enlarged statistical framework compared to generalized linear models (GLMs) with which to approach regression modelling. Comprising of about half-a-dozen major classes of

The purpose of this book is to define a broad spectrum of statistical linear models that is useful in the analysis of data. Considered a "classic" for years, the material in this book is now

This book presents a greatly enlarged statistical framework compared to generalized linear models (GLMs) with which to approach regression modelling. Comprising of about half-a-dozen major classes of

Focusing on robust rank-based nonparametric methods, this book covers rank-based fitting and testing for models ranging from simple location models to general linear models for uncorrelated and correl

Suitable for researchers and graduate students in the social sciences, this book shows how to apply spatial models using R to a wide range of data from political science and related areas. It also cov

This graduate-level guide introduces concepts, assumptions, and practical implementation procedures in hierarchical linear modeling (also known as multilevel modeling or linear mixed modeling). The fi

Multivariate General Linear Models is an integrated introduction to multivariate multiple regression analysis (MMR) and multivariate analysis of variance (MANOVA). Beginning with an overview of the un

This volume contains articles focusing on the use of linear algebra as an indispensable tool in researching Markov chains and queueing models. Markov chains and queueing models play an increasingly im

In teaching linear statistical models to first-year graduate students or to final-year undergraduate students there is no way to proceed smoothly without matrices and related concepts of linear algebr

This new edition of Stochastic Linear Programming: Models, Theory and Computation has been brought completely up to date, either dealing with or at least referring to new material on models and method

Linear Algebra: An Introduction With Mathematica uses a matrix-based presentation and covers the standard topics any mathematician will need to understand linear algebra while using Mathematica. Devel