Unlimited

Authorized users

Can lend chapters, not whole ebooks

Linear Model Theory : With Examples and Exercises / by Dale L. Zimmerman.

1st ed. 2020.

3030520633
9783030520632
3030520625
9783030520625

Cham : Springer International Publishing : Imprint: Springer, 2020.

English

1 online resource (XXI, 504 p. 14 illus.)

10.1007/978-3-030-52063-2 doi

QA276-280

519.5

This textbook presents a unified and rigorous approach to best linear unbiased estimation and prediction of parameters and random quantities in linear models, as well as other theory upon which much of the statistical methodology associated with linear models is based. The single most unique feature of the book is that each major concept or result is illustrated with one or more concrete examples or special cases. Commonly used methodologies based on the theory are presented in methodological interludes scattered throughout the book, along with a wealth of exercises that will benefit students and instructors alike. Generalized inverses are used throughout, so that the model matrix and various other matrices are not required to have full rank. Considerably more emphasis is given to estimability, partitioned analyses of variance, constrained least squares, effects of model misspecification, and most especially prediction than in many other textbooks on linear models. This book is intended for master and PhD students with a basic grasp of statistical theory, matrix algebra and applied regression analysis, and for instructors of linear models courses. Solutions to the book's exercises are available in the companion volume Linear Model Theory - Exercises and Solutions by the same author.

Access limited to authorized users.

Online Resources > Ebooks
All Resources