Analysis of Longitudinal Data and millions of other books are available for Amazon . of Longitudinal Data (Oxford Statistical Science Series) by Peter Diggle. : Analysis of Longitudinal Data (): Peter J. Diggle, Kung-Yee Liang, Scott L. Zeger: Books. Longitudinal Data Analysis. Peter Diggle Time series and longitudinal data: similarities/differences. 2. Linear models: Analysis of Bailrigg temperature data.
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The main topics discussed are design issues, exploratory methods of analysis, linear models for continuous data, general linear models for discrete data, and models and methods for handling data and missing values. Book ratings by Goodreads.
Analysis of longitudinal data – Peter Diggle, Kung-Yee Liang, Scott L. Zeger – Google Books
This book is available as part of Oxford Scholarship Online – view abstracts and keywords at analysi and chapter level. Describing the statistical models and methods for the analysis of longitudinal data, it covers both the underlying statistical theory of each method, and its application to a range of examples from the agricultural and biomedical Oxford University Press Amazon.
Likelihood-based methods for categorical data ; Analysis of variance methods 7.
Exploring longitudinal data ; 4. We’re featuring millions of their reader ratings on our book pages to help you find your new favourite book.
Looking for beautiful books? They have also chosen a good selection of examples, many of them medical, with which the various methods are clearly illustrated. It belongs to the possession of every statistician who encounters longitudinal data. Visit our Beautiful Books page and find lovely books for kids, photography lovers and more.
This second edition, published for the first time in paperback, provides a thorough and expanded revision of pfter important text.
Analysis of Longitudinal Data : Peter J. Diggle :
Check out the top books of the year on our page Best Books of Analysis of Longitudinal Data Second Edition Peter Diggle, Patrick Heagerty, Kung-Yee Liang, and Scott Zeger Oxford Statistical Science Series Completely revised and expanded to become the most up-to-date and thorough professional reference text Includes design issues, exploratory methods of analysis, linear models for continuous data, and models and methods for handling data and missing values.
Two new chapters have been added on fully parametric models for discrete repeated measures data and on statistical models for time-dependent predictors where llngitudinal may be feedback between the predictor and response variables. To purchase, visit your preferred ebook provider. Exploring longitudinal data 4.
Symbolic Computation for Statistical Inference D. My library Help Advanced Book Search.
The book is readable and well written Goodreads is the world’s largest site for readers with over 50 million reviews. Academic Skip to main content. Under each heading, worked examples are presented in parallel with the methodological development, and sufficient detail is given to enable the reader to reproduce the author’s results using the data-sets as an appendix. Missing values in longitudinal data.
Generalized linear models for longitudinal data 8.
Describing the statistical models and methods for the analysis of longitudinal data, it covers both the underlying statistical theory of each method, and its application to a range of examples from the agricultural and biomedical Parametric models for covariance structure 6. The main topics discussed are design issues, exploratory methods of analysis, linear models for continuous data, general linear models for discrete data, and models and methods for handling data and missing values.
Ebook This title is available as an ebook. Additional topics ; Appendix ; Bibliography ; Index show more. Helpfully, they also mention the topics that they have chosen not to present, together with other recommended books for you to follow up Random effects models ; My library Help Advanced Book Search.
Analysis of Longitudinal Data
Infectious Diseases of Humans Roy M. It furthers the University’s objective of excellence in research, scholarship, and education by publishing worldwide. Analysis of variance methods.
Parametric models for covariance structure.