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Linear Models and the Relevant Distributions and Matrix Algebra
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Artikel-Nr:
9780367572037
Veröffentl:
2020
Erscheinungsdatum:
30.06.2020
Seiten:
538
Autor:
David A Harville
Gewicht:
939 g
Format:
251x175x30 mm
Sprache:
Englisch
Beschreibung:
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.
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.
Introduction. Matrix Algebra: a Primer. Random Vectors and Matrices. The General Linear Model. Estimation and Prediction: Classical Approach. Some Relevant Distributions and Their Properties. Confidence Intervals (or Sets) and Tests of Hypotheses.