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＜電子ブック＞
Modeling Longitudinal Data / by Robert E. Weiss
(Springer Texts in Statistics. ISSN:21974136)

版	1st ed. 2005.
出版者	(New York, NY : Springer New York : Imprint: Springer)
出版年	2005
本文言語	英語
大きさ	XXII, 432 p : online resource
著者標目	*Weiss, Robert E author SpringerLink (Online service)
件　名	LCSH:Epidemiology LCSH:Social sciences LCSH:Humanities LCSH:Biometry LCSH:Statistics  LCSH:Social sciences -- Statistical methods  全ての件名で検索 FREE:Epidemiology FREE:Humanities and Social Sciences FREE:Biostatistics FREE:Statistical Theory and Methods FREE:Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy FREE:Statistics in Business, Management, Economics, Finance, Insurance
一般注記	to Longitudinal Data -- Plots -- Simple Analyses -- Critiques of Simple Analyses -- The Multivariate Normal Linear Model -- Tools and Concepts -- Specifying Covariates -- Modeling the Covariance Matrix -- Random Effects Models -- Residuals and Case Diagnostics -- Discrete Longitudinal Data -- Missing Data -- Analyzing Two Longitudinal Variables -- Further Reading Longitudinal data are ubiquitous across Medicine, Public Health, Public Policy, Psychology, Political Science, Biology, Sociology and Education, yet many longitudinal data sets remain improperly analyzed. This book teaches the art and statistical science of modern longitudinal data analysis. The author emphasizes specifying, understanding, and interpreting longitudinal data models. He inspects the longitudinal data graphically, analyzes the time trend and covariates, models the covariance matrix, and then draws conclusions. Covariance models covered include random effects, autoregressive, autoregressive moving average, antedependence, factor analytic, and completely unstructured models among others. Longer expositions explore: an introduction to and critique of simple non-longitudinal analyses of longitudinal data, missing data concepts, diagnostics, and simultaneous modeling of two longitudinal variables. Applications and issues for random effects models cover estimation, shrinkage, clustered data, models for binary and count data and residuals and residual plots. Shorter sections include a general discussion of how computational algorithms work, handling transformed data, and basic design issues. This book requires a solid regression course as background and is particularly intended for the final year of a Biostatistics or Statistics Masters degree curriculum. The mathematical prerequisite is generally low, mainly assuming familiarity with regression analysis in matrix form. Doctoral students in Biostatistics or Statistics, applied researchers and quantitative doctoral students in disciplines such as Medicine, Public Health, Public Policy, Psychology, Political Science, Biology, Sociology and Education will find this book invaluable. The book has many figures and tables illustrating longitudinal data and numerous homework problems. The associated web site contains many longitudinal data sets, examples of computer code, and labs to re-enforce the material. Robert Weiss is Professor of Biostatistics in the UCLA School of Public Health with a Ph.D. in Statistics from the University of Minnesota. He is expert in longitudinal data analysis, diagnostics and graphics, and Bayesian methods, and specializes in modeling of hierarchical and complex data sets. He has published over 50 papers a majority of which involves longitudinal data. He regularly teaches classes in longitudinal data analysis, multivariate analysis, Bayesian inference, and statistical graphics HTTP:URL=https://doi.org/10.1007/0-387-28314-5

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