Nonparametric and Semiparametric Models

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, flexibilit...

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Main Authors: Härdle, Wolfgang Karl, Müller, Marlene (Author), Sperlich, Stefan (Author), Werwatz, Axel (Author)
Corporate Author: SpringerLink (Online service)
Format: eBook
Language:English
Published: Berlin, Heidelberg Springer Berlin Heidelberg 2004, 2004
Edition:1st ed. 2004
Series:Springer Series in Statistics
Subjects:
Online Access:
Collection: Springer Book Archives -2004 - Collection details see MPG.ReNa
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245 0 0 |a Nonparametric and Semiparametric Models  |h Elektronische Ressource  |c by Wolfgang Karl Härdle, Marlene Müller, Stefan Sperlich, Axel Werwatz 
250 |a 1st ed. 2004 
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300 |a XXVII, 300 p  |b online resource 
505 0 |a 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 
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653 |a Probability Theory and Stochastic Processes 
653 |a Probabilities 
700 1 |a Müller, Marlene  |e [author] 
700 1 |a Sperlich, Stefan  |e [author] 
700 1 |a Werwatz, Axel  |e [author] 
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520 |a 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. The material is easy to accomplish since the e-book character of the text gives a maximum of flexibility in learning (and teaching) intensity