“…provides a systematic discussion about statistical methods and theory for handling linear regression analysis when such conventional model assumptions are violated.” (Technometrics, November 2005)
“…an accessible introduction to GLSE…an excellent source of reference, can be used as a course text, and will help to stimulate further research into this flourishing topic…” (Mathematical Reviews, 2005)
“…provides an up-to-date, self-contained introduction to the unified theory …” (Zentralblatt Math, Vol. 1057, No. 8. 2005)
Regression analysis has been one of the most widely used statistical tools for many years, and continues to be developed and applied to new applications. Generalized least squares estimation (GLSE) based on Gauss-Markov theory plays a key role in understanding theoretical and practical aspects of statistical inference in general linear regression models. GLSE can be applied to problems encountered in many disciplines, particularly statistics, econometrics, and biometrics.
- Provides a self-contained introduction to GLSE.
- Includes detailed coverage of the ‘lower and upper bounds’ approach, pioneered by the authors.
- Adopts a concise yet mathematically rigorous approach.
- Includes applications to statistics, econometrics, and biometrics.
- Contains exercises at the end of each chapter, enabling use as a course text or for self-study.
- Includes a comprehensive bibliography.
Generalized Least Squares provides an accessible introduction to GLSE suitable for researchers and graduate students from statistics, econometrics, and biometrics. It provides an excellent source of reference, can be used as a course text, and will help to stimulate further research into this flourishing topic.
From the Back Cover
Regression analysis has been one of the most widely used statistical tools for many years, and continues to be developed and applied to new applications. Generalized least squares estimation (GLSE) based on Gauss-Markov theory plays a key role in understanding theoretical and practical aspects of statistical inference in general linear regression models. GLSE can be applied to problems encountered in many disciplines, particularly statistics, econometrics, and biometrics.
- Provides a self-contained introduction to GLSE.
- Includes detailed coverage of the ‘lower and upper bounds’ approach, pioneered by the authors.
- Adopts a concise yet mathematically rigorous approach.
- Includes applications to statistics, econometrics, and biometrics.
- Contains exercises at the end of each chapter, enabling use as a course text or for self-study.
- Includes a comprehensive bibliography.
Generalized Least Squares provides an accessible introduction to GLSE suitable for researchers and graduate students from statistics, econometrics, and biometrics. It provides an excellent source of reference, can be used as a course text, and will help to stimulate further research into this flourishing topic.
About the Author
Takeaki Kariya is the author of Generalized Least Squares, published by Wiley.
Hiroshi Kurata is the author of Generalized Least Squares, published by Wiley.
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