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statistiek --- Mathematical statistics --- 519.2 --- #ABIB:astp --- 519.2 Probability. Mathematical statistics --- Probability. Mathematical statistics
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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) --- Analyse de variance --- Modèles linéaires (Statistique) --- #TELE:SISTA --- Models, Linear (Statistics) --- Mathematical models --- Statistics --- ANOVA (Analysis of variance) --- Variance analysis --- Experimental design --- Analysis of variance. --- Linear models (Statistics). --- Modèles linéaires (Statistique)
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Log-linear models --- Modèles log-linéaires --- Log-linear models. --- #SBIB:303H520 --- AA / International- internationaal --- 305.971 --- Models, Log-linear --- Multivariate analysis --- Regression analysis --- Methoden sociale wetenschappen: techniek van de analyse, algemeen --- Speciale gevallen in econometrische modelbouw. --- Modèles log-linéaires --- Speciale gevallen in econometrische modelbouw
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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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As the new title indicates, this second edition of Log-Linear Models has been modi?ed to place greater emphasis on logistic regression. In addition to new material, the book has been radically rearranged. The fundamental material is contained in Chapters 1-4. Intermediate topics are presented in Chapters 5 through 8. Generalized linear models are presented in Ch- ter 9. The matrix approach to log-linear models and logistic regression is presented in Chapters 10-12, with Chapters 10 and 11 at the applied Ph.D. level and Chapter 12 doing theory at the Ph.D. level. The largest single addition to the book is Chapter 13 on Bayesian bi- mial regression. This chapter includes not only logistic regression but also probit and complementary log-log regression. With the simplicity of the Bayesian approach and the ability to do (almost) exact small sample s- tistical inference, I personally ?nd it hard to justify doing traditional large sample inferences. (Another possibility is to do exact conditional inference, but that is another story.) Naturally,Ihavecleaneduptheminor?awsinthetextthatIhavefound. All examples, theorems, proofs, lemmas, etc. are numbered consecutively within each section with no distinctions between them, thus Example 2.3.1 willcomebeforeProposition2.3.2.Exercisesthatdonotappearinasection at the end have a separate numbering scheme. Within the section in which it appears, an equation is numbered with a single value, e.g., equation (1).
Log-linear models. --- Mathematical statistics --- 519.2 --- #SBIB:303H520 --- #SBIB:303H61 --- #PBIB:1999.1 --- 519.2 Probability. Mathematical statistics --- Probability. Mathematical statistics --- Methoden sociale wetenschappen: techniek van de analyse, algemeen --- Wiskundige methoden en technieken --- Mathematics. --- Algebra. --- Applied mathematics. --- Engineering mathematics. --- Probabilities. --- Probability Theory and Stochastic Processes. --- Applications of Mathematics. --- Log-linear models --- Models, Log-linear --- Multivariate analysis --- Regression analysis --- Distribution (Probability theory. --- Mathematics --- Mathematical analysis --- Engineering --- Engineering analysis --- Probability --- Statistical inference --- Combinations --- Chance --- Least squares --- Risk
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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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Now in its third edition, this companion volume to Ronald Christensen’s Plane Answers to Complex Questions uses three fundamental concepts from standard linear model theory—best linear prediction, projections, and Mahalanobis distance— to extend standard linear modeling into the realms of Statistical Learning and Dependent Data. This new edition features a wealth of new and revised content. In Statistical Learning it delves into nonparametric regression, penalized estimation (regularization), reproducing kernel Hilbert spaces, the kernel trick, and support vector machines. For Dependent Data it uses linear model theory to examine general linear models, linear mixed models, time series, spatial data, (generalized) multivariate linear models, discrimination, and dimension reduction. While numerous references to Plane Answers are made throughout the volume, Advanced Linear Modeling can be used on its own given a solid background in linear models. Accompanying R code for the analyses is available online.
Linear models (Statistics) --- Models, Linear (Statistics) --- Mathematical models --- Mathematical statistics --- 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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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
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