000			02074nam a2200277Ia 4500
003			OSt
005			20251119101414.0
008			220909b |||||||| |||| 00| 0 eng d
020			_a9781107009653
037			_cTextbook
040			_aCSL _beng _cCSL
041			_aeng
084			_aB284 Q2 TB _qCSL
100			_a Tutz, Gerhard _eauthor _9852160
245		0	_aRegression for Categorical Data
260			_aCambridge : _bCambridge , _c2012 .
300			_ax, 561p.
490			_aCambridge series in statistical and probabilistic mathematics
500			_aIncluded Bibliography 513-544p.; Author index 545-553p.; Subject index 554-561p.
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, which provide excellent tools for prediction and the handling of both nominal and ordered categorical predictors. The book is accompanied an R package that contains data sets and code for all the examples.
650			_aCategorical data. _9852161
650			_aRegression analysis. _9852162
650			_aMathematics. _9852163
942			_hB284 Q2 TB _cTB _2CC _n0
999			_c7303 _d7303