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This is the second edition of Linear Models for Multivariate, Time Series and Spatial Data. It has a new title to indicate that it contains much new material. The primary changes are the addition of two new chapters: one on nonparametric regression and one on response surface maximization. As before, the presentations focus on the linear model aspects of the subject. For example, in the nonparametric regression chapter there is very little about kernal regression estimation but quite a bit about series approxi­ mations, splines, and regression trees, all of which can be viewed as linear modeling. The new edition also includes various smaller changes. Of particular note are a subsection in Chapter 1 on modeling longitudinal (repeated measures) data and a section in Chapter 6 on covariance structures for spatial lattice data. I would like to thank Dale Zimmerman for the suggestion of incor­ porating material on spatial lattices. Another change is that the subject index is now entirely alphabetical.

Mathematical statistics --- 519.2 --- Linear models (Statistics) --- Models, Linear (Statistics) --- Mathematical models --- Statistics --- Probability. Mathematical statistics --- Basic Sciences. Statistics --- Mathematical Statistics --- 519.2 Probability. Mathematical statistics --- Linear models (Statistics). --- Mathematical Statistics. --- Probabilities. --- Computer mathematics. --- Statistics . --- Probability Theory and Stochastic Processes. --- Computational Mathematics and Numerical Analysis. --- Statistical Theory and Methods. --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- Computer mathematics --- Electronic data processing --- Probability --- Statistical inference --- Combinations --- Chance --- Least squares --- Risk

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Mathematical statistics --- Analysis of variance. --- Linear models (Statistics) --- Linear models (Statistics).

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This 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 not 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 nonadditivity test.

Analysis of variance. --- Linear models (Statistics). --- Linear models (Statistics) --- Analysis of variance --- Mathematics --- Physical Sciences & Mathematics --- Mathematical Statistics --- ANOVA (Analysis of variance) --- Variance analysis --- Models, Linear (Statistics) --- Statistics. --- Statistical Theory and Methods. --- Mathematical statistics --- Experimental design --- Mathematical models --- Statistics --- Mathematical statistics. --- Statistical inference --- Statistics, Mathematical --- Probabilities --- Sampling (Statistics) --- Statistical methods --- Statistics . --- Statistical analysis --- Statistical data --- Statistical science --- Econometrics

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Lineaire modellen (Statistiek) --- Mathematical statistics --- Linear models (Statistics) --- Linear models (Statistics). --- Modèles linéaires (Statistique) --- Time-series analysis --- Modèles linéaires (statistique) --- Séries chronologiques --- Modèles linéaires (statistique) --- Séries chronologiques

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This 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: estimation including biased and Bayesian estimation, significance testing, ANOVA, multiple comparisons, regression analysis, and experimental design models. In addition, the book covers topics that are not usually treated at this level, but which are important in their own right: best linear and best linear unbiased prediction, split plot models, balanced incomplete block designs, testing for lack of fit, testing for independence, models with singular covariance matrices, diagnostics, collinearity, and variable selection. This new edition includes new sections on alternatives to least squares estimation and the variance-bias tradeoff, expanded discussion of variable selection, new material on characterizing the interaction space in an unbalanced two-way ANOVA, Freedman's critique of the sandwich estimator, and much more.

Statistics . --- Statistical Theory and Methods. --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- Linear models (Statistics) --- Models, Linear (Statistics) --- Mathematical models --- Mathematical statistics --- Statistics