000			02759nam a2200325 a 4500
003			arcduce
005			20230914214946.0
008			100622s2004 gw_||||| |||| 00| 0 eng d
020			_a3540207228
040			_aarcduce _carcduce
082	0		_a519.54
245	0	0	_aNonparametric and semiparametric models / _cWolfgang Härdle ... [et al.].
260			_aBerlin : _bSpringer-Verlag, _c2004
300			_axxvii, 299 p. : _bil.
490	0		_aSpringer series in statistics
504			_aIncluye referencias bibliograficas. Bibliografía: p. 279-290
505	0		_a1 Introduction -- 1.1 Density Estimation -- 1.2 Regression -- Summary -- I Nonparametric Models -- 2 Histogram -- 3 Nonparametric Density Estimation -- 4 Nonparametric Regression -- II Semiparametric Models -- 5 Semiparametric and Generalized Regression Models -- 6 Single Index Models -- 7 Generalized Partial Linear Models -- 8 Additive Models and Marginal Effects -- 9 Generalized Additive Models -- References -- Author Index.
520	3		_aThe concept of nonparametric smoothing is a central idea in statistics that aims to simultaneously estimate and modes the underlying structure. The book considers high dimensional objects, as density functions and regression. The semiparametric modeling technique compromises the two aims, flexibility and simplicity of statistical procedures, by introducing partial parametric components. These components allow to match structural conditions like e.g. linearity in some variables and may be used to model the influence of discrete variables. The aim of this monograph is to present the statistical and mathematical principles of smoothing with a focus on applicable techniques. The necessary mathematical treatment is easily understandable and a wide variety of interactive smoothing examples are given. The book does naturally split into two parts: Nonparametric models (histogram, kernel density estimation, nonparametric regression) and semiparametric models (generalized regression, single index models, generalized partial linear models, additive and generalized additive models). The first part is intended for undergraduate students majoring in mathematics, statistics, econometrics or biometrics whereas the second part is intended to be used by master and PhD students or researchers.
650		4	_aESTADISTICA MATEMATICA _91599
650		4	_aPROBABILIDADES _91598
650		4	_aECONOMETRIA _986
650		4	_aESTADISTICA NO PARAMETRICA _92912
700	1		_aHärdle, Wolfgang Karl, _d1953-, _eautor _915289
700			_aMüller, Marlene
700			_aWerwatz, Axel
700			_aSperlich, Stefan
856	4		_uhttps://ar1lib.org/book/2135948/215341 _qtexto/pdf 2.23 MB
942			_cLIBR _j519.54 N 51146 _2ddc
945			_aCRA
999			_c22013 _d22013