Sophia Rabe-Hesketh – författare

This book unifies and extends latent variable models, including multilevel or generalized linear mixed models, longitudinal or panel models, item response or factor models, latent class or finite mixture models, and structural equation models. Following a gentle introduction to latent variable modeling, the authors clearly explain and contrast a wi

Each chapter will consist of basic statistical theory, simple examples of S-PLUS code, more complex examples of S-PLUS code, and exercises. All data sets will be taken from genuine medical investigations and will be made available, if possible, on a web site. All examples will contain extensive graphical analysis to highlight one of the prime features of S-PLUS. The book would complement Venables and Ripley (VR). However, there is far less about the details of S-PLUS and probably less technical descriptions of techniques. The book concentrates solely on medical data sets trying to demonstrate the flexibility of S-PLUS and its huge advantages, particularly for applied medical statisticians.

This book unifies and extends latent variable models, including multilevel or generalized linear mixed models, longitudinal or panel models, item response or factor models, latent class or finite mixture models, and structural equation models. Following a gentle introduction to latent variable modeling, the authors clearly explain and contrast a wi

With each new release of Stata, a comprehensive resource is needed to highlight the improvements as well as discuss the fundamentals of the software. Fulfilling this need, AHandbook of Statistical Analyses Using Stata, Fourth Edition has been fully updated to provide an introduction to Stata version 9. This edition covers many new features of Stata, including a new command for mixed models and a new matrix language.Each chapter describes the analysis appropriate for a particular application, focusing on the medical, social, and behavioral fields. The authors begin each chapter with descriptions of the data and the statistical techniques to be used. The methods covered include descriptives, simple tests, variance analysis, multiple linear regression, logistic regression, generalized linear models, survival analysis, random effects models, and cluster analysis. The core of the book centers on how to use Stata to perform analyses and how to interpret the results. The chapters conclude with several exercises based on data sets from different disciplines. A concise guide to the latest version of Stata, A Handbook of Statistical Analyses Using Stata, Fourth Edition illustrates the benefits of using Stata to perform various statistical analyses for both data analysis courses and self-study.

Each chapter will consist of basic statistical theory, simple examples of S-PLUS code, more complex examples of S-PLUS code, and exercises. All data sets will be taken from genuine medical investigations and will be made available, if possible, on a web site. All examples will contain extensive graphical analysis to highlight one of the prime features of S-PLUS. The book would complement Venables and Ripley (VR). However, there is far less about the details of S-PLUS and probably less technical descriptions of techniques. The book concentrates solely on medical data sets trying to demonstrate the flexibility of S-PLUS and its huge advantages, particularly for applied medical statisticians.

Each chapter will consist of basic statistical theory, simple examples of S-PLUS code, more complex examples of S-PLUS code, and exercises. All data sets will be taken from genuine medical investigations and will be made available, if possible, on a web site. All examples will contain extensive graphical analysis to highlight one of the prime features of S-PLUS. The book would complement Venables and Ripley (VR). However, there is far less about the details of S-PLUS and probably less technical descriptions of techniques. The book concentrates solely on medical data sets trying to demonstrate the flexibility of S-PLUS and its huge advantages, particularly for applied medical statisticians.

This book unifies and extends latent variable models, including multilevel or generalized linear mixed models, longitudinal or panel models, item response or factor models, latent class or finite mixture models, and structural equation models. Following a gentle introduction to latent variable modeling, the authors clearly explain and contrast a wide range of estimation and prediction methods from biostatistics, psychometrics, econometrics, and statistics. They present exciting and realistic applications that demonstrate how researchers can use latent variable modeling to solve concrete problems in areas as diverse as medicine, economics, and psychology. The examples considered include many nonstandard response types, such as ordinal, nominal, count, and survival data. Joint modeling of mixed responses, such as survival and longitudinal data, is also illustrated. Numerous displays, figures, and graphs make the text vivid and easy to read.About the authors: Anders Skrondal is Professor and Chair in Social Statistics, Department of Statistics, London School of Economics, UK Sophia Rabe-Hesketh is a Professor of Educational Statistics at the Graduate School of Education and Graduate Group in Biostatistics, University of California, Berkeley, USA.

With each new release of Stata, a comprehensive resource is needed to highlight the improvements as well as discuss the fundamentals of the software. Fulfilling this need, AHandbook of Statistical Analyses Using Stata, Fourth Edition has been fully updated to provide an introduction to Stata version 9. This edition covers many new features of Stata, including a new command for mixed models and a new matrix language.Each chapter describes the analysis appropriate for a particular application, focusing on the medical, social, and behavioral fields. The authors begin each chapter with descriptions of the data and the statistical techniques to be used. The methods covered include descriptives, simple tests, variance analysis, multiple linear regression, logistic regression, generalized linear models, survival analysis, random effects models, and cluster analysis. The core of the book centers on how to use Stata to perform analyses and how to interpret the results. The chapters conclude with several exercises based on data sets from different disciplines. A concise guide to the latest version of Stata, A Handbook of Statistical Analyses Using Stata, Fourth Edition illustrates the benefits of using Stata to perform various statistical analyses for both data analysis courses and self-study.

Multilevel and Longitudinal Modeling Using Stata, Fourth Edition, is a complete resource for learning to model data in which observations are grouped—whether those groups are formed by a nesting structure, such as children nested in classrooms, or formed by repeated observations on the same individuals. This text introduces random-effects models, fixed-effects models, mixed-effects models, marginal models, dynamic models, and growth-curve models, all of which account for the grouped nature of these types of data. As Rabe-Hesketh and Skrondal introduce each model, they explain when the model is useful, its assumptions, how to fit and evaluate the model using Stata, and how to interpret the results. With this comprehensive coverage, researchers who need to apply multilevel models will find this book to be the perfect companion. It is also the ideal text for courses in multilevel modeling because it provides examples from a variety of disciplines as well as end-of-chapter exercises that allow students to practice newly learned material.The book comprises two volumes. Volume I focuses on linear models for continuous outcomes, while volume II focuses on generalized linear models for binary, ordinal, count, and other types of outcomes.

Multilevel and Longitudinal Modeling Using Stata, Fourth Edition, is a complete resource for learning to model data in which observations are grouped. With comprehensive coverage, researchers who need to apply multilevel models will find this book to be the perfect companion. It is also the ideal text for courses in multilevel modeling because it provides examples from a variety of disciplines as well as end-of-chapter exercises that allow students to practice newly learned material. The book comprises two volumes. Volume I focuses on linear models for continuous outcomes.