Isbn: 9780521844727 - elements of distribution theory (cambridge series in statistical and probabilistic mathematics) (6 Ergebnisse)

Zustand: Good. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In good all round condition. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,1150grams, ISBN:9780521844727.

Zustand: New. In English.

Zustand: Good. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In good all round condition. No dust jacket. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,1150grams, ISBN:9780521844727. …

Zustand: New. This introduction to distribution theory uses no measure theory, making it suitable for students in statistics and econometrics and researchers using statistical methods. Series Editor(s): Gill, R.; Ripley, Brian D.; Ross, S.; Silverman, B.W. (St Peter's College, Oxford); Stein, M. Series: Cambridge Series in Statistical and Probabilistic Mathematics. Num Pages: 528 pages, 10 tables 354 exercises. BIC Classification: PB. Category: (P) Professional & Vocational; (U) Tertiary Education (US: College). Dimension: 253 x 177 x 29. Weight in Grams: 1075. . 2005. Illustrated. hardcover. . . . . Books ship from the US and Ireland. …

Hardcover. Zustand: Brand New. 515 pages. 10.50x7.50x1.50 inches. In Stock.

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This 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.…