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020 _a9780387479460
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024 7 _a10.1007/978-0-387-47946-0
_2doi
040 _aISI Library, Kolkata
050 4 _aQA273.A1-274.9
050 4 _aQA274-274.9
072 7 _aPBT
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072 7 _aMAT029000
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072 7 _aPBT
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072 7 _aPBWL
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082 0 4 _a519.2
_223
100 1 _aJiang, Jiming.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aLinear and Generalized Linear Mixed Models and Their Applications
_h[electronic resource] /
_cby Jiming Jiang.
264 1 _aNew York, NY :
_bSpringer New York,
_c2007.
300 _aXIV, 257 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringer Series in Statistics,
_x0172-7397
505 0 _aLinear Mixed Models: Part I -- Linear Mixed Models: Part II -- Generalized Linear Mixed Models: Part I -- Generalized Linear Mixed Models: Part II.
520 _aThis book covers two major classes of mixed effects models, linear mixed models and generalized linear mixed models, and it presents an up-to-date account of theory and methods in analysis of these models as well as their applications in various fields. The book offers a systematic approach to inference about non-Gaussian linear mixed models. Furthermore, it has included recently developed methods, such as mixed model diagnostics, mixed model selection, and jackknife method in the context of mixed models. The book is aimed at students, researchers and other practitioners who are interested in using mixed models for statistical data analysis. The book is suitable for a course in a M.S. program in statistics, provided that the section of further results and technical notes in each of the first four chapters is skipped. If these four sections are included, the book may be used for a course in a Ph. D. program in statistics. A first course in mathematical statistics, the ability to use computers for data analysis, and familiarity with calculus and linear algebra are prerequisites. Additional statistical courses such as regression analysis and a good knowledge about matrices would be helpful. Jiming Jiang is Professor of Statistics and Director of the Statistical Laboratory at UC-Davis. He is a prominent researcher in the fields of mixed effects models and small area estimation, and co-receiver of the Chinese National Natural Science Award and American Statistical Association's Outstanding Statistical Application Award.
650 0 _aDistribution (Probability theory.
650 0 _aMathematical statistics.
650 0 _aNumerical analysis.
650 0 _aGenetics
_xMathematics.
650 1 4 _aProbability Theory and Stochastic Processes.
_0http://scigraph.springernature.com/things/product-market-codes/M27004
650 2 4 _aStatistical Theory and Methods.
_0http://scigraph.springernature.com/things/product-market-codes/S11001
650 2 4 _aPublic Health.
_0http://scigraph.springernature.com/things/product-market-codes/H27002
650 2 4 _aNumerical Analysis.
_0http://scigraph.springernature.com/things/product-market-codes/M14050
650 2 4 _aGenetics and Population Dynamics.
_0http://scigraph.springernature.com/things/product-market-codes/M31010
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781441923684
776 0 8 _iPrinted edition:
_z9780387516738
776 0 8 _iPrinted edition:
_z9780387479415
830 0 _aSpringer Series in Statistics,
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856 4 0 _uhttps://doi.org/10.1007/978-0-387-47946-0
912 _aZDB-2-SMA
942 _cEB
950 _aMathematics and Statistics (Springer-11649)
999 _c425455
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