Download Advanced Linear Models (Statistics: A Series of Textbooks by Shein-Chung Chow, Song-Gui Wang PDF

By Shein-Chung Chow, Song-Gui Wang

This paintings information the statistical inference of linear versions together with parameter estimation, speculation checking out, self assurance durations, and prediction. The authors speak about the appliance of statistical theories and methodologies to numerous linear types reminiscent of the linear regression version, the research of variance version, the research of covariance version, and the variance elements version.

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Extra resources for Advanced Linear Models (Statistics: A Series of Textbooks and Monographs)

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PU][DP]1994[/DP]End of Citation  < previous page < previous page next page > next page > page_iv page_ix Page ix Part III Applications 7. 6 Regression Diagnostics 335 Supplements and Problems 357 8. 6 Other Models 422 Supplements and Problems 424 9. 4 Analysis of Variance with Missing Data 444 Supplements and Problems 455 10. [/PU][DP]1994[/DP]End of Citation  < previous page < previous page page_ix page_v next page > next page > 45 46 Page v authors and are not necessarily those of the University of Science and Technology of China or Bristol-Myers Squibb Company.

A. Henry, and S. L. Barker for their support and encouragement and Drs. J. Bergum, H. L. Ju, and F. Y. C. Ki for many helpful discussions and for reading portions of the manuscript. The first author wishes to express many thanks to his wife, Zhong-Zhen Jia, and two daughters, Xiao-Jing and Xiao-Tian, for their assistance in providing many necessary references at the early stages of the preparation of this book. The second author wishes to express his appreciation to his wife, Yueh-Ji, and two daughters, Emily and Lilly, for their patience and understanding during the preparation of this book.

PU][DP]1994[/DP]End of Citation  < previous page < previous page page_iii page_iv next page > next page > Page iv and prediction. In the third part of the book, the methodologies are applied to various linear models, such as the linear regression model, the analysis of variance model, the analysis of covariance model, and the variance components model. Unlike other books on linear models, this book explores each of these models using a unified approach. In each chapter, an up-to-date theory, methodology, applications, and further developments are included.

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