This textbook presents a unified and rigorous approach to best linear unbiased estimation and prediction of parameters and random quantities in linear models, as well as other theory upon which much of the statistical methodology associated with linear models is based. The single most unique feature of the book is that each major concept or result is illustrated with one or more concrete examples or special cases. Commonly used methodologies based on the theory are presented in methodological interludes scattered throughout the book, along with a wealth of exercises that will benefit students and instructors alike. Generalized inverses are used throughout, so that the model matrix and various other matrices are not required to have full rank. Considerably more emphasis is given to estimability, partitioned analyses of variance, constrained least squares, effects of model misspecification, and most especially prediction than in many other textbooks on linear models. This book is intended for master and PhD students with a basic grasp of statistical theory, matrix algebra and applied regression analysis, and for instructors of linear models courses. Solutions to the book's exercises are available in the companion volume Linear Model Theory - Exercises and Solutions by the same author.

In the past, disease pattern mapping depended on census tracts based on political units, such as states and counties. However, with the advent of geographic information systems (GIS), researchers can now achieve a new level of precision and flexibility in geographic locating. This emerging technology allows the mapping of many different kinds of geographies, including disease rates in relation to pollution sources. Geocoding Health Data presents a state-of-the-art discussion on the current technical and administrative developments in geographic information science. In particular, it discusses how geocoded residential addresses can be used to examine the spatial patterns of cancer incidence, staging, survival, and mortality. The book begins with an introduction of various codes and their uses, including census geographic, health area, and street level codes. It goes on to describe the specific application of geocodes to cancer, detailing methods, materials, and technical issues. The text illustrates how to compile data maps for analysis and addresses issues, such as mismatch correction and data quality. It describes the current state of geocoding practices and discusses the use of individually geocoded cancer incidences in spatial epidemiology, distance estimation and spatial accessibilities, and tips for handling non-geocoded cases. Special consideration is given to privacy and confidentiality issues by focusing on disclosure limitation methods. With recent disease outbreaks and escalating concerns about bioterrorism, interest in the application of GIS to individual data is growing. The fundamental concepts presented by this book are of great value to anyone trying to understand the causes, prevention, and control of cancer as well as a variety of other diseases.

The First Book Dedicated to This Class of Longitudinal Models Although antedependence models are particularly useful for modeling longitudinal data that exhibit serial correlation, few books adequately cover these models. By gathering results scattered throughout the literature, Antedependence Models for Longitudinal Data offers a convenient, systematic way to learn about antedependence models. Illustrated with numerous examples, the book also covers some important statistical inference procedures associated with these models. After describing unstructured and structured antedependence models and their properties, the authors discuss informal model identification via simple summary statistics and graphical methods. They then present formal likelihood-based procedures for normal antedependence models, including maximum likelihood and residual maximum likelihood estimation of parameters as well as likelihood ratio tests and penalized likelihood model selection criteria for the model's covariance structure and mean structure. The authors also compare the performance of antedependence models to other models commonly used for longitudinal data. With this book, readers no longer have to search across widely scattered journal articles on the subject. The book provides a thorough treatment of the properties and statistical inference procedures of various antedependence models.

In the past, disease pattern mapping depended on census tracts based on political units, such as states and counties. However, with the advent of geographic information systems (GIS), researchers can now achieve a new level of precision and flexibility in geographic locating. This emerging technology allows the mapping of many different kinds of geographies, including disease rates in relation to pollution sources. Geocoding Health Data presents a state-of-the-art discussion on the current technical and administrative developments in geographic information science. In particular, it discusses how geocoded residential addresses can be used to examine the spatial patterns of cancer incidence, staging, survival, and mortality. The book begins with an introduction of various codes and their uses, including census geographic, health area, and street level codes. It goes on to describe the specific application of geocodes to cancer, detailing methods, materials, and technical issues. The text illustrates how to compile data maps for analysis and addresses issues, such as mismatch correction and data quality. It describes the current state of geocoding practices and discusses the use of individually geocoded cancer incidences in spatial epidemiology, distance estimation and spatial accessibilities, and tips for handling non-geocoded cases. Special consideration is given to privacy and confidentiality issues by focusing on disclosure limitation methods. With recent disease outbreaks and escalating concerns about bioterrorism, interest in the application of GIS to individual data is growing. The fundamental concepts presented by this book are of great value to anyone trying to understand thecauses, prevention, and control of cancer as well as a variety of other diseases.

This book contains 296 exercises and solutions covering a wide variety of topics in linear model theory, including generalized inverses, estimability, best linear unbiased estimation and prediction, ANOVA, confidence intervals, simultaneous confidence intervals, hypothesis testing, and variance component estimation. The models covered include the Gauss-Markov and Aitken models, mixed and random effects models, and the general mixed linear model. Given its content, the book will be useful for students and instructors alike. Readers can also consult the companion textbook Linear Model Theory - With Examples and Exercises by the same author for the theory behind the exercises.

This book contains 296 exercises and solutions covering a wide variety of topics in linear model theory, including generalized inverses, estimability, best linear unbiased estimation and prediction, ANOVA, confidence intervals, simultaneous confidence intervals, hypothesis testing, and variance component estimation. The models covered include the Gauss-Markov and Aitken models, mixed and random effects models, and the general mixed linear model. Given its content, the book will be useful for students and instructors alike. Readers can also consult the companion textbook Linear Model Theory - With Examples and Exercises by the same author for the theory behind the exercises.

This textbook presents a unified and rigorous approach to best linear unbiased estimation and prediction of parameters and random quantities in linear models, as well as other theory upon which much of the statistical methodology associated with linear models is based. The single most unique feature of the book is that each major concept or result is illustrated with one or more concrete examples or special cases. Commonly used methodologies based on the theory are presented in methodological interludes scattered throughout the book, along with a wealth of exercises that will benefit students and instructors alike. Generalized inverses are used throughout, so that the model matrix and various other matrices are not required to have full rank. Considerably more emphasis is given to estimability, partitioned analyses of variance, constrained least squares, effects of model misspecification, and most especially prediction than in many other textbooks on linear models. This book is intended for master and PhD students with a basic grasp of statistical theory, matrix algebra and applied regression analysis, and for instructors of linear models courses. Solutions to the book's exercises are available in the companion volume Linear Model Theory - Exercises and Solutions by the same author.