Incluye referencias bibliograficas. Bibliografía: p. 279-290

1 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.

The 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.