Design of Experiments | The Elements of Statistical Learning | Bayesian Data Analysis | Foundations of Linear and Generalized Linear Models | Nonparametric Statistical Methods | Theory of Point Estimation | Probability and Statistical Inference | Markov Chains | Linear Models with R
A First Course in Linear Model Theory systematically presents the basic theory behind linear statistical models with motivation from an algebraic as well as a geometric perspective. Through the concepts and tools of matrix and linear algebra and distribution theory, it provides a framework for understanding classical and contemporary linear model theory. It does not merely introduce formulas, but develops in students the art of statistical thinking and inspires learning at an intuitive level by emphasizing conceptual understanding.
The authors' fresh approach, methodical presentation, wealth of examples, and introduction to topics beyond the classical theory set this book apart from other texts on linear models. It forms a refreshing and invaluable first step in students' study of advanced linear models, generalized linear models, nonlinear models, and dynamic models.
About: This innovative, intermediate-level statistics text fills an important gap by presenting the theory of linear statistical models at a level appropriate for senior undergraduate or first-year graduate students.
Pricing is shown for items sent to or within the U.S., excluding shipping and tax. Please consult the store to determine exact fees. No warranties are made express or implied about the accuracy, timeliness, merit, or value of the information provided. Information subject to change without notice. isbn.nu is not a bookseller, just an information source.