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About this product
- DescriptionThis 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 analysis of variance models and regression models, based on projections, orthogonality, and other vector space ideas. Every chapter comes with numerous exercises and examples that make it ideal for a graduate-level course. All of the standard topics are covered in depth: ANOVA, estimation including Bayesian estimation, hypothesis testing, multiple comparisons, regression analysis, and experimental design models. In addition, the book covers topics that are t usually treated at this level, but which are important in their own right: balanced incomplete block designs, testing for lack of fit, testing for independence, models with singular covariance matrices, variance component estimation, best linear and best linear unbiased prediction, collinearity, and variable selection. This new edition includes a more extensive discussion of best prediction and associated ideas of R2, as well as new sections on inner products and perpendicular projections for more general spaces and Milliken and Graybill's generalization of Tukey's one degree of freedom for nadditivity test.
- Author BiographyRonald Christensen is Professor of Statistics at the University of New Mexico, Fellow of the American Statistical Association (ASA) and the Institute of Mathematical Statistics, and former Chair of the ASA Section on Bayesian Statistical Science.
- Author(s)Ronald Christensen
- PublisherSpringer-Verlag New York Inc.
- Date of Publication15/07/2013
- Series TitleSpringer Texts in Statistics
- Place of PublicationNew York, NY
- Country of PublicationUnited States
- ImprintSpringer-Verlag New York Inc.
- Content Note18 black & white tables, biography
- Weight783 g
- Width155 mm
- Height235 mm
- Spine27 mm
- Edition StatementSoftcover reprint of hardcover 4th ed. 2011
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