000 02345nam a2200361 i 4500
001 CR9780511842061
003 UkCbUP
005 20240919190242.0
006 m|||||o||d||||||||
007 cr||||||||||||
008 101025s2012||||enk o ||1 0|eng|d
020 _a9780511842061 (ebook)
020 _z9781107009653 (hardback)
040 _aUkCbUP
_beng
_erda
_cUkCbUP
050 0 0 _aQA278.2
_b.T88 2012
082 0 0 _a519.5/36
_222
100 1 _aTutz, Gerhard,
_eauthor.
245 1 0 _aRegression for categorical data /
_cGerhard Tutz.
264 1 _aCambridge :
_bCambridge University Press,
_c2012.
300 _a1 online resource (x, 561 pages) :
_bdigital, PDF file(s).
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
490 1 _aCambridge series on statistical and probabilistic mathematics ;
_v34
500 _aTitle from publisher's bibliographic system (viewed on 05 Oct 2015).
520 _aThis book introduces basic and advanced concepts of categorical regression with a focus on the structuring constituents of regression, including regularization techniques to structure predictors. In addition to standard methods such as the logit and probit model and extensions to multivariate settings, the author presents more recent developments in flexible and high-dimensional regression, which allow weakening of assumptions on the structuring of the predictor and yield fits that are closer to the data. A generalized linear model is used as a unifying framework whenever possible in particular parametric models that are treated within this framework. Many topics not normally included in books on categorical data analysis are treated here, such as nonparametric regression; selection of predictors by regularized estimation procedures; ternative models like the hurdle model and zero-inflated regression models for count data; and non-standard tree-based ensemble methods. The book is accompanied by an R package that contains data sets and code for all the examples.
650 0 _aRegression analysis.
650 0 _aCategories (Mathematics)
776 0 8 _iPrint version:
_z9781107009653
830 0 _aCambridge series on statistical and probabilistic mathematics ;
_v34.
856 4 0 _uhttps://doi.org/10.1017/CBO9780511842061
942 _2ddc
_cEB
999 _c9859
_d9859