1st Edition

Linear Models and the Relevant Distributions and Matrix Algebra A Unified Approach Volume 2

By David A. Harville Copyright 2024
    242 Pages 3 B/W Illustrations
    by Chapman & Hall

    242 Pages 3 B/W Illustrations
    by Chapman & Hall

    Linear Models and the Relevant Distributions and Matrix Algebra: A Unified Approach, Volume 2 covers several important topics that were not included in the first volume. The second volume complements the first, providing detailed solutions to the exercises in both volumes, thereby greatly enhancing its appeal for use in advanced statistics programs. This volume can serve as a valuable reference. It can also serve as a resource in a mathematical statistics course for use in illustrating various theoretical concepts in the context of a relatively complex setting of great practical importance. Together with the first volume, this volume provides a largely self-contained treatment of an important area of statistics and should prove highly useful to graduate students and others.

    Key Features:

    • Includes solutions to the exercises from both the first and second volumes
    • Includes coverage of several topics not covered in the first volume
    • Highly valuable as a reference book for graduate students and researchers

    Part 1: Additional Topics  8. Constrained Linear Models and Related Topics  Part 2: Exercises and Solutions  2. Matrix Algebra: A Primer  3. Random Vectors and Matrices  4. The General Linear Model  5. Estimation and Prediction: Classical Approach  6. Some Relevant Distributions and Their Properties  7. Confidence Intervals (or Sets) and Tests of Hypotheses  8. Constrained Linear Models and Related Topics


    David A. Harville served for 10 years as a mathematical statistician in the Applied Mathematics Research Laboratory of the Aerospace Research Laboratories (at Wright-Patterson AFB, Ohio), for 20 years as a full professor in Iowa State University’s Department of Statistics (where he now has emeritus status), and 7 years as a research staff member of the Mathematical Sciences Department of IBM’s T.J. Watson Research Center. He has extensive experience in the area of linear statistical models, having taught (on numerous occasions) M.S. and Ph.D. level courses on that subject, having been the thesis advisor of 10 Ph.D. graduates, and having authored (or co-authored) 3 books and more than 80 research articles. His work has been recognized through his election as a Fellow of the American Statistical Association and of the Institute of Mathematical Statistics and as a member of the International Statistical Institute.