Nonparametric and Semiparametric Models (Springer Series in Statistics) - Hardcover

Über die Autorin bzw. den Autor

Wolfgang Karl Härdle is the Ladislaus von Bortkiewicz Professor of Statistics at the Humboldt-Universität zu Berlin, Germany. He is also a Professor at the Faculty of Mathematics and Physics at the Charles University in Prague, Czech Republic. He teaches quantitative finance and semi-parametric statistics. His research focuses on modern machine learning, multivariate statistics in finance and computational statistics. He is an elected member of the ISI (International Statistical Institute) and the director of the Institute of Digital Assets, Academy of Economic Sciences, Bucharest, Romania. Léopold Simar is an Emeritus Professor of Statistics at UCLouvain, Louvain-la-Neuve, Belgium. He has taught mathematical statistics, multivariate analysis, bootstrap methods in statistics and econometrics at several European universities. His research focuses on non-parametric and semi-parametric methods and bootstrap techniques in statistics and econometrics. He is an elected member of the ISI and the past president of the Belgian Statistical Society and is a regular Visiting Professor at the Sapienza University of Rome, Italy and at the Toulouse School of Economics, France. Matthias R. Fengler is a Professor of Econometrics at the School of Economics and Political Science at the University of St. Gallen, Switzerland. His area of specialization is Financial Econometrics and he works in asset pricing, volatility modeling, risk-management, and the analysis of financial text data. In collaboration with colleagues from both academia and industry, he initiated the University of St. Gallen's Impact Award-winning project Monitoring Consumption Switzerland, which analyzes and explores private debit and credit card expenditures and payment behavior in Switzerland.

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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.