Elements of Distribution Theory
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Elements of Distribution Theory by Thomas A. Severini
Book DescriptionThis detailed introduction to distribution theory uses no measure theory, making it suitable for students in statistics and econometrics as well as for researchers who use statistical methods. Good backgrounds in calculus and linear algebra are important and a course in elementary mathematical analysis is useful, but not required. An appendix gives a detailed summary of the mathematical definitions and results that are used in the book. Topics covered range from the basic distribution and density functions, expectation, conditioning, characteristic functions, cumulants, convergence in distribution and the central limit theorem to more advanced concepts such as exchangeability, models with a group structure, asymptotic approximations to integrals, orthogonal polynomials and saddlepoint approximations. The emphasis is on topics useful in understanding statistical methodology; thus, parametric statistical models and the distribution theory associated with the normal distribution are covered comprehensively.
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Book DetailsISBN: 9780521844727
(253mm x 177mm x 29mm)
Imprint: Cambridge University Press
Publisher: Cambridge University Press
Publish Date: 8-Aug-2005
Country of Publication: United Kingdom
Books By Author Thomas A. Severini
Elements of Distribution Theory, Paperback (October 2011)
This introduction to distribution theory uses no measure theory, making it suitable for students in statistics and econometrics and researchers using statistical methods.
Likelihood Methods in Statistics, Hardback (November 2000)» View all books by Thomas A. Severini
Likelihood methods play a central role in statistical theory and methodology. Recently, a new approach to likelihood inference has been developed that often leads to substantial improvements over classical methods. This book gives a detailed introduction to this modern theory of likelihood methods.
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Author Biography - Thomas A. Severini
Thomas A. Severini received his PhD in Statistics from the University of Chicago. He is now a Professor of Statistics at Northwestern University. He has also written Likelihood Methods in Statistics. He has published extensively in statistical journals such as Biometrika, the Journal of the American Statistical Association and the Journal of the Royal Statistical Society. He is a member of the Institute of Mathematical Statistics and the American Statistical Association.
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