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Modelling Frequency and Count Data (Oxford Statistical Science Series)

Modelling Frequency and Count Data (Oxford Statistical Science Series)

Modelling Frequency and Count Data (Oxford Statistical Science Series)
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Modelling Frequency and Count Data (Oxford Statistical Science Series) Hardback - 1995

by Lindsey, J. K

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OXFORD UNIVERSITY PRESS, 1995-06-15. 1. hardcover. Used: Good. 9.02x5.98x0.81. Buy with confidence. Excellent Customer Service & Return policy.
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Details

  • Title Modelling Frequency and Count Data (Oxford Statistical Science Series)
  • Author Lindsey, J. K
  • Binding Hardback
  • Edition 1
  • Condition Used: Good
  • Pages 300
  • Volumes 1
  • Language ENG
  • Publisher OXFORD UNIVERSITY PRESS, Oxford
  • Publication date 1995-06-15
  • Illustrated Yes
  • Features Bibliography, Illustrated, Index
  • Bookseller's Inventory # SONG0198523319
  • ISBN 9780198523314 / 0198523319
  • Weight 1.31 lbs (0.59 kg)
  • Dimensions 9.28 x 6.32 x 0.98 in (23.57 x 16.05 x 2.49 cm)
  • Size 9.02x5.98x0.81
  • Category Mathematics
  • Library of Congress subjects Multivariate analysis
  • Library of Congress Catalogue Number 94046457
  • Dewey Decimal Code 519.536
  • Quantity available 1

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Reader reviews for Modelling Frequency and Count Data (Oxford Statistical Science Series)

From the publisher

Categorical data analysis is a special area of generalized linear models, which has become the most important area of statistical applications in many disciplines, from medicine to social sciences. This text presents the standard models and many newly developed ones in a language that can be immediately applied in many modern statistical packages such as GLIM, GENSTAT, S-Plus, as well as SAS and LISP-STAT. The book is structure around the distinction between independent events occurring to different individuals, resulting in frequencies, and repeated events occurring to the same individuals, yielding counts. The book demonstrates that much of modern statistics can be seen as special cases of categorical data models; both generalized linear models and proportional hazards models can be fitted as log linear models. More specialized topics such as Markov chains, overdispersion and random effects, are also covered.
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