David A. Harville

Linear Models and the Relevant Distributions and Matrix Algebra (eBook, PDF)

• Format: PDF

Produktbeschreibung

Using linear statistical models as a basis for statistical inference and the theoretical underpinnings of resultant inferential procedures. Includes topics typically covered less extensively; prediction, multiple-comparison procedures for controlling FDR, spherical/elliptical distributions.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 538
• Erscheinungstermin: 22. März 2018
• Englisch
• ISBN-13: 9781351264679
• Artikelnr.: 54009746

Autorenporträt

David 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, 20 years as a full professor in Iowa State University's Department of Statistics where he now has emeritus status, and seven years as a research staff member of the Mathematical Sciences Department of IBM's T.J. Watson Research Center. He has considerable relevant experience, having taught M.S. and Ph.D. level courses in linear models, been the thesis advisor of 10 Ph.D. graduates, and authored or co-authored two 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.

Inhaltsangabe

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

Rezensionen

"The book presents procedures for making statistical inferences on the basis of the classical linear statistical model, and discusses the various properties of those procedures. Supporting material on matrix algebra and statistical distributions is interspersed with a discussion of relevant inferential procedures and their properties. The coverage ranges from MS-level to advanced researcher. In particular, the material in chapters 6-7 is not covered in an approachable manner in any other books, and greatly generalizes the traditional normal-based linear regression model to the elliptical distributions, thus greatly elucidating the advanced reader on just how far this class of models can be extended. Refreshingly, the material also goes beyond the classical 20th century coverage to include some 21st century topics like microarray (big) data analysis, and control of false discovery rates in large scale experiments...From the point of view of an advanced instructor and researcher on the subject, I very strongly recommend publication...Note that...this book provides the coverage of 3 books, hence the title purporting to provide a 'unified approach' (of 3 related subjects) is indeed accurate."
~Alex Trindade, Texas Tech University

"The book is very well written, with exceptional attention to details. It provides detailed derivations or proofs of almost all the results, and offers in-depth coverage of the topics discussed. Some of these materials (e.g., spherical/elliptical distributions) are hard to find from other sources. Anyone who is interested in linear models should benefit from reading this book and find it especially useful for a thorough understanding of the linear-model theory in a unified framework... The book is a delight to read."
~Huaiqing Wu, Iowa State University

"This book is useful in two ways: an excellent text book for a graduate level linear models course, and for those who want to learn linear mod