Introduction to the Theory of Statistical Inference: Chapman & Hall/CRC Texts in Statistical Science
Autor Hannelore Lieroen Limba Engleză Paperback – 20 iul 2011
Highlights
- Basic notations and ideas of statistical inference are explained in a mathematically rigorous, but understandable, form
- Classroom-tested and designed for students of mathematical statistics
- Examples, applications of the general theory to special cases, exercises, and figures provide a deeper insight into the material
- Solutions provided for problems formulated at the end of each chapter
- Combines the theoretical basis of statistical inference with a useful applied toolbox that includes linear models
- Theoretical, difficult, or frequently misunderstood problems are marked
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Specificații
ISBN-13: 9781439852927
ISBN-10: 1439852928
Pagini: 284
Ilustrații: 8 Tables, black and white; 40 Illustrations, black and white
Dimensiuni: 156 x 234 x 14 mm
Greutate: 0.41 kg
Ediția:1
Editura: CRC Press
Colecția Chapman and Hall/CRC
Seria Chapman & Hall/CRC Texts in Statistical Science
ISBN-10: 1439852928
Pagini: 284
Ilustrații: 8 Tables, black and white; 40 Illustrations, black and white
Dimensiuni: 156 x 234 x 14 mm
Greutate: 0.41 kg
Ediția:1
Editura: CRC Press
Colecția Chapman and Hall/CRC
Seria Chapman & Hall/CRC Texts in Statistical Science
Public țintă
Undergraduate students in introductory statistical inference courses; students and researchers in statistics and related fields.Recenzii
… it provides in-depth explanations, complete with proofs, of how statistics works. … The book has several user-friendly aspects. One is the use of eight example data sets to illustrate the theory throughout the text. This repeated use of the same examples allows readers to focus their energy on applying a theoretical point under discussion to a familiar example rather than having to first become acquainted with a new example. Another big help are the detailed solutions provided for the problems that appear at the end of each chapter. … Also helpful: Theoretical or difficult material that can be skipped is marked with an asterisk. … a clear exposition of the theory of statistical inference, along with complete proofs and familiar examples. The text analyzes not just methods one learns in a first statistics course, but alternatives as well. Each chapter is capped by a further reading section that is at once comprehensive and concise.
—David A. Huckaby, MAA Reviews, February 2012
—David A. Huckaby, MAA Reviews, February 2012
Cuprins
Introduction. Statistical Model. Inference principles. Estimation. Testing Hypotheses. Linear Model. Solutions. Bibliography. Index.
Notă biografică
Hannelore Liero is an apl. Prof. for Mathematical Statistics at the University of Potsdam. She studied Mathematics at the Humboldt-University in Berlin. She earned her Ph.D. while working as a scientist at the Academy of Sciences of the GDR. Since 1992, she has taught Statistics for undergraduate and graduate students in Mathematics, Biology and Computer Science at the Faculty of Sciences at the University of Potsdam. In addition to teaching, she does basic research in Statistics and supports scientists applying statistical methods in practice. Silvelyn Zwanzig is an Associate Professor for Mathematical Statistics at Uppsala University. She studied Mathematics at the Humboldt-University in Berlin. Before coming to Sweden she was Assistant Professor at the University of Hamburg in Germany. She received her Ph.D. in Mathematics at the Academy of Sciences of the GDR. Since 1991, she has taught Statistics for undergraduate and graduate students. Her research interests have moved from theoretical statistics to computer intensive statistics. She is interested in consulting and was working in Astrometry.
Descriere
Based on the authors’ lecture notes, this text presents concise yet complete coverage of statistical inference theory, focusing on the fundamental classical principles. Unlike related textbooks, it combines the theoretical basis of statistical inference with a useful applied toolbox that includes linear models. Suitable for a second semester undergraduate course on statistical inference, the text offers proofs to support the mathematics and does not require any use of measure theory. It illustrates core concepts using cartoons and provides solutions to all examples and problems.