Nonparametric and Semiparametric Models - Couverture rigide

Hardle, Wolfgang; Muller, Marlene; Sperlich, Stefan; Werwatch, Axel

Couverture rigide

ISBN 10 : 3540207228 ISBN 13 : 9783540207221

Editeur : Springer-Verlag Berlin and Heidelberg GmbH & Co. K, 2004

Synopsis

<p>The statistical and mathematical principles of smoothing with a focus on applicable techniques are presented in this book. It naturally splits into two parts: 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.</p>

A propos du livre

À propos de l'auteur

Wolfgang Karl Härdle is the Ladislaus von Bortkiewicz Professor of Statistics at the Humboldt-Universität zu Berlin and director of C.A.S.E. (Center for Applied Statistics and Economics), director of the CRC-649 (Collaborative Research Center) "Economic Risk" and director of the IRTG 1792 "High Dimensional Non-stationary Time Series". He teaches quantitative finance and semi-parametric statistics.  His research focuses on dynamic factor models, multivariate statistics in finance and computational statistics. He is an elected member of the ISI (International Statistical Institute) and advisor to the Guanghua School of Management, Peking University and a senior fellow of Sim Kee Boon Institute of Financial Economics at the Singapore Management University. Sigbert Klinke is a postdoctoral research fellow at the Ladislaus von Bortkiewicz Chair of Statistics at Humboldt-Universität zu Berlin. He received his PhD in computational statistics from the Catholique University in Louvain-la-Neuve, Belgium. He teaches introductory statistics courses and data analytical courses for bachelor and master students in Economics and Educational Science at Humboldt-Universität zu Berlin's School of Business and Economics. His research focuses on computational and multivariate statistics and the teaching of statistics.  Bernd Rönz was a Professor of Statistics at the Institute for Statistics and Econometrics, School of Business and Economics, Humboldt University, Berlin. He taught Statistics, Computational Statistics and Generalized Linear Models. His research focused on multivariate statistics, computational statistics and generalized linear models. He previously worked as Associate Professor of Quantitative Methods for Business Decisions at the University of Dar es Salaam, Tanzania for more than two years. Furthermore, he was a Visiting Lecturer at Hosei-University Tokyo and Ritsumeikan-University Kyoto and a Visiting Fellow at the Centre for Mathematics and its Applications, School of Mathematical Sciences, The Australian National University, Canberra. He retired in 2006.

À propos de la quatrième de couverture

<p>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.</p><p>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.</p><p>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.</p><p>The material is easy to accomplish since the e-book character of the text gives a maximum of flexibility in learning (and teaching) intensity.</p>

Détails de l'édition

• Éditeur: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
• Date d'édition: 2004
• Langue: anglais
• ISBN 10: 3540207228
• ISBN 13: 9783540207221
• Reliure: Relié
• Nombre de pages: 297
• Coordonnées du fabricant: non disponible
• Personne responsable: non disponible