| 000 | 04624nam a22005175i 4500 | ||
|---|---|---|---|
| 001 | 978-0-387-28314-2 | ||
| 003 | DE-He213 | ||
| 005 | 20161121230925.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 100301s2005 xxu| s |||| 0|eng d | ||
| 020 |
_a9780387283142 _9978-0-387-28314-2 |
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| 024 | 7 |
_a10.1007/0-387-28314-5 _2doi |
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| 050 | 4 | _aRA648.5-654 | |
| 072 | 7 |
_aMBNS _2bicssc |
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| 072 | 7 |
_aMED028000 _2bisacsh |
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| 082 | 0 | 4 |
_a614.4 _223 |
| 100 | 1 |
_aWeiss, Robert E. _eauthor. |
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| 245 | 1 | 0 |
_aModeling Longitudinal Data _h[electronic resource] / _cby Robert E. Weiss. |
| 264 | 1 |
_aNew York, NY : _bSpringer New York, _c2005. |
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| 300 |
_aXXII, 432 p. _bonline resource. |
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| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 347 |
_atext file _bPDF _2rda |
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| 490 | 1 |
_aSpringer Texts in Statistics, _x1431-875X |
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| 505 | 0 | _ato 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. | |
| 520 | _aLongitudinal 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. | ||
| 650 | 0 | _aMedicine. | |
| 650 | 0 | _aScience. | |
| 650 | 0 | _aEpidemiology. | |
| 650 | 0 | _aStatistics. | |
| 650 | 1 | 4 | _aMedicine & Public Health. |
| 650 | 2 | 4 | _aEpidemiology. |
| 650 | 2 | 4 | _aScience, general. |
| 650 | 2 | 4 | _aStatistics for Life Sciences, Medicine, Health Sciences. |
| 650 | 2 | 4 | _aStatistical Theory and Methods. |
| 650 | 2 | 4 | _aStatistics for Social Science, Behavorial Science, Education, Public Policy, and Law. |
| 650 | 2 | 4 | _aStatistics for Business/Economics/Mathematical Finance/Insurance. |
| 710 | 2 | _aSpringerLink (Online service) | |
| 773 | 0 | _tSpringer eBooks | |
| 776 | 0 | 8 |
_iPrinted edition: _z9780387402710 |
| 830 | 0 |
_aSpringer Texts in Statistics, _x1431-875X |
|
| 856 | 4 | 0 | _uhttp://dx.doi.org/10.1007/0-387-28314-5 |
| 912 | _aZDB-2-SMA | ||
| 950 | _aMathematics and Statistics (Springer-11649) | ||
| 999 |
_c506006 _d506006 |
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