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OverviewFull Product DetailsAuthor: Rolf Sundberg (Stockholms Universitet)Publisher: Cambridge University Press Imprint: Cambridge University Press Dimensions: Width: 15.20cm , Height: 1.70cm , Length: 22.80cm Weight: 0.430kg ISBN: 9781108701112ISBN 10: 1108701116 Pages: 296 Publication Date: 29 August 2019 Audience: College/higher education , Postgraduate, Research & Scholarly Format: Paperback Publisher's Status: Active Availability: Available To Order ![]() We have confirmation that this item is in stock with the supplier. It will be ordered in for you and dispatched immediately. Table of ContentsReviews'Rolf Sundberg's book gives attractive properties of the exponential family and illustrates them for a wide variety of applications. Definitions are concise and most propositions look directly appealing. The writing reflects the author's experience in deriving results that are essential for good modelling and convincing inference. Thus, this book is indispensable for all data scientists, be they graduate students or experienced researchers.' Nanny Wermuth, Chalmers tekniska hoegskola, Sweden 'Rolf Sundberg's book gives attractive properties of the exponential family and illustrates them for a wide variety of applications. Definitions are concise and most propositions look directly appealing. The writing reflects the author's experience in deriving results that are essential for good modelling and convincing inference. Thus, this book is indispensable for all data scientists, be they graduate students or experienced researchers.' Nanny Wermuth, Chalmers tekniska hoegskola, Sweden 'Rolf Sundberg's book gives attractive properties of the exponential family and illustrates them for a wide variety of applications. Definitions are concise and most propositions look directly appealing. The writing reflects the author's experience in deriving results that are essential for good modelling and convincing inference. Thus, this book is indispensable for all data scientists, be they graduate students or experienced researchers.' Nanny Wermuth, Chalmers tekniska hoegskola, Sweden 'This is an excellent book on exponential families. It covers not only the basic properties of exponential families but also several modern topics such as graphical models and random networks. The author blends theories and applications elegantly and provides several useful examples from various scientific domains. It is suitable for a one-semester graduate-level course and will be an excellent reference for topic courses such as stochastic modeling and parametric models.' Yen-Chi Chen, Journal of the American Statistical Association 'Overall, this is a clearly written, graduate-level introduction to an important area of statistical modelling. The numerous examples and exercises included throughout provide invaluable illustrations across a number of application areas, making this a useful reference for both researchers and practitioners. As a textbook, it is an excellent starting point for either a taught course on statistical inference with an emphasis on data from the exponential family, or for self-directed study in this area.' Fraser Daly, Institute of Mathematical Statistics Textbooks 'This book is perfect for an introductory theoretical graduate course but its parts could also definitely be used in a more applied course. The only prerequisite is basic mathematical statistics. The book is also very handy as a general reference on exponential families. To keep the content simple, the author sometimes avoids the most technical details; however, all necessary references are provided for the reader's convenience. In this sense the book can be used by any researcher interested in exponential families from either a more theoretical or more applied point of view.' Piotr Zwiernik, MathSciNet 'Rolf Sundberg's book gives attractive properties of the exponential family and illustrates them for a wide variety of applications. Definitions are concise and most propositions look directly appealing. The writing reflects the author's experience in deriving results that are essential for good modelling and convincing inference. Thus, this book is indispensable for all data scientists, be they graduate students or experienced researchers.' Nanny Wermuth, Chalmers tekniska h gskola, Sweden 'Rolf Sundberg's book gives attractive properties of the exponential family and illustrates them for a wide variety of applications. Definitions are concise and most propositions look directly appealing. The writing reflects the author's experience in deriving results that are essential for good modelling and convincing inference. Thus, this book is indispensable for all data scientists, be they graduate students or experienced researchers.' Nanny Wermuth, Chalmers tekniska hoegskola, Sweden 'This is an excellent book on exponential families. It covers not only the basic properties of exponential families but also several modern topics such as graphical models and random networks. The author blends theories and applications elegantly and provides several useful examples from various scientific domains. It is suitable for a one-semester graduate-level course and will be an excellent reference for topic courses such as stochastic modeling and parametric models.' Yen-Chi Chen, Journal of the American Statistical Association 'Overall, this is a clearly written, graduate-level introduction to an important area of statistical modelling. The numerous examples and exercises included throughout provide invaluable illustrations across a number of application areas, making this a useful reference for both researchers and practitioners. As a textbook, it is an excellent starting point for either a taught course on statistical inference with an emphasis on data from the exponential family, or for self-directed study in this area.' Fraser Daly, Institute of Mathematical Statistics Textbooks Author InformationRolf Sundberg is Professor Emeritus of Statistical Science at Stockholms Universitet. His work embraces both theoretical and applied statistics, including principles of statistics, exponential families, regression, chemometrics, stereology, survey sampling inference, molecular biology, and paleoclimatology. In 2003, with M. Linder, he won the award for best theoretical paper in the Journal of Chemometrics for their work on multivariate calibration, and in 2017 he was named Statistician of the Year by the Swedish Statistical Society. Tab Content 6Author Website:Countries AvailableAll regions |