000 02165nam a22003017a 4500
003 arcduce
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007 t|
008 121101s2017 flu||||| |||| 00| 0 eng d
020 _a9781498728331
040 _aarcduce
_carcduce
082 0 _222
_a519.282
100 1 _94129
_aWood, Simon N.
245 1 0 _aGeneralized additive models :
_ban introduction with R /
_cSimon N. Wood.
250 _a2nd ed.
260 _aBoca Raton, Fl. :
_bCRC Press,
_c2017
300 _axx, 476 p.
490 0 _aTexts in statistical science
504 _aBibliografía: p. 455-465.
505 0 _aPreface -- 1. Linear models -- 2. Linear models -- 3. Generalized linear models -- 4. Introducing GAMs -- 5. Smoothers -- 6. GAM theory -- 7. GAMs in practice: mgcv -- A: Maximum likelihood estimation -- B: Some matrix algebra -- C: Solutions to exercises -- Bibliography.
520 3 _aThe first edition of this book has established itself as one of the leading references on generalized additive models (GAMs), and the only book on the topic to be introductory in nature with a wealth of practical examples and software implementation. It is self-contained, providing the necessary background in linear models, linear mixed models, and generalized linear models (GLMs), before presenting a balanced treatment of the theory and applications of GAMs and related models. The author bases his approach on a framework of penalized regression splines, and while firmly focused on the practical aspects of GAMs, discussions include fairly full explanations of the theory underlying the methods. Use of R software helps explain the theory and illustrates the practical application of the methodology. Each chapter contains an extensive set of exercises, with solutions in an appendix or in the book’s R data package gamair, to enable use as a course text or for self-study.
650 4 _aMODELO LINEAL
_9362
650 4 _aEJERCICIOS DE ESTADISTICA
_9475
856 4 _uhttps://ar1lib.org/book/3553646/73cc0c
856 4 _uhttps://orcid.org/0000-0002-2034-7453
_yInformación sobre el autor
942 _2ddc
_cLIBR
_j519.282 W 56619
945 _aBEA
_c2021-10-25
999 _c30168
_d30168