An Introduction to the Advanced Theory and Practice of Nonparametric Econometrics: A Replicable Approach Using R, Jeffrey S. Racine

Автор: Ghosal, Subhashis.
Название: Fundamentals of Nonparametric Bayesian Inference
ISBN: 0521878268 ISBN-13(EAN): 9780521878265
Издательство: Cambridge Academ
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Цена: 86590.00 T
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Описание: Written by top researchers, this self-contained text is the authoritative account of Bayesian nonparametrics, a nearly universal framework for inference in statistics and machine learning, with practical use in all areas of science, including economics and biostatistics. Appendices with prerequisites and numerous exercises support its use for graduate courses.

Автор: Henderson
Название: Applied Nonparametric Econometrics
ISBN: 110701025X ISBN-13(EAN): 9781107010253
Издательство: Cambridge Academ
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Цена: 116160.00 T
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Описание: The majority of empirical research in economics ignores the potential benefits of nonparametric methods, while the majority of advances in nonparametric theory ignore the problems faced in applied econometrics. This book helps bridge this gap between applied economists and theoretical nonparametric econometricians, discussing basic to advanced nonparametric methods with applications.

Автор: Joel L. Horowitz
Название: Semiparametric and Nonparametric Methods in Econometrics
ISBN: 1461429277 ISBN-13(EAN): 9781461429272
Издательство: Springer
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Цена: 130430.00 T
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Описание: This text emphasizes the main ideas underlying a variety of nonparametric and semiparametric methods. This edition contains over one hundred pages of new material as well as empirical examples to illustrate the methods presented.

Автор: Henderson
Название: Applied Nonparametric Econometrics
ISBN: 0521279682 ISBN-13(EAN): 9780521279680
Издательство: Cambridge Academ
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Цена: 44350.00 T
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Описание: The majority of empirical research in economics ignores the potential benefits of nonparametric methods, while the majority of advances in nonparametric theory ignore the problems faced in applied econometrics. This book helps bridge this gap between applied economists and theoretical nonparametric econometricians, discussing basic to advanced nonparametric methods with applications.

Автор: Racine, Jeffrey; Su, Liangjun; Ullah, Aman
Название: The Oxford Handbook of Applied Nonparametric and Semiparametric Econometrics and Statistics
ISBN: 0199857946 ISBN-13(EAN): 9780199857944
Издательство: Oxford Academ
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Цена: 153120.00 T
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Описание: This volume, edited by Jeffrey Racine, Liangjun Su, and Aman Ullah, contains the latest research on nonparametric and semiparametric econometrics and statistics. Chapters by leading international econometricians and statisticians highlight the interface between econometrics and statistical methods for nonparametric and semiparametric procedures.

Автор: Alexandre B. Tsybakov
Название: Introduction to Nonparametric Estimation
ISBN: 1441927093 ISBN-13(EAN): 9781441927095
Издательство: Springer
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Цена: 102480.00 T
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Описание: Developed from lecture notes and ready to be used for a course on the graduate level, this concise text aims to introduce the fundamental concepts of nonparametric estimation theory while maintaining the exposition suitable for a first approach in the field.

Автор: Hale Douglas, Lady George, Maybee John
Название: Nonparametric Comparative Statics and Stability
ISBN: 0691603189 ISBN-13(EAN): 9780691603186
Издательство: Wiley
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Цена: 42240.00 T
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Описание: The authors, leading researchers in the fields of mathematical economics and methodology, present the first comprehensive synthesis of literature on qualitative and other nonparametric techniques, which are important elements of comparative statics and stability analysis in economic theory. The topics covered show how to assess the comparative stat

Автор: Fr?d?ric Ferraty; Philippe Vieu
Название: Nonparametric Functional Data Analysis
ISBN: 1441921419 ISBN-13(EAN): 9781441921413
Издательство: Springer
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Цена: 121110.00 T
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Описание: At the same time it shows how functional data can be studied through parameter-free statistical ideas, and offers an original presentation of new nonparametric statistical methods for functional data analysis.

Автор: Simar Leopold
Название: Estimation and Inference in Nonparametric Frontier Models
ISBN: 1601986661 ISBN-13(EAN): 9781601986665
Издательство: Неизвестно
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Цена: 91040.00 T
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Описание: Provides a thorough examination of this topic for students and researchers alike. While nonparametric estimators are widely used to estimate the productive efficiency of firms and other organizations, it is often done without any attempt to make statistical inference.

Автор: Cao
Название: Nonparametric Statistics
ISBN: 3319415816 ISBN-13(EAN): 9783319415819
Издательство: Springer
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Цена: 102480.00 T
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Описание: This volume collects selected, peer-reviewed contributions from the 2nd Conference of the International Society for Nonparametric Statistics (ISNPS), held in C?diz (Spain) between June 11–16 2014, and sponsored by the American Statistical Association, the Institute of Mathematical Statistics, the Bernoulli Society for Mathematical Statistics and Probability, the Journal of Nonparametric Statistics and Universidad Carlos III de Madrid.The 15 articles are a representative sample of the 336 contributed papers presented at the conference. They cover topics such as high-dimensional data modelling, inference for stochastic processes and for dependent data, nonparametric and goodness-of-fit testing, nonparametric curve estimation, object-oriented data analysis, and semiparametric inference.The aim of the ISNPS 2014 conference was to bring together recent advances and trends in several areas of nonparametric statistics in order to facilitate the exchange of research ideas, promote collaboration among researchers from around the globe, and contribute to the further development of the field.

Автор: Fan Jianqing, Yao Qiwei
Название: Nonlinear Time Series / Nonparametric and Parametric Methods
ISBN: 0387261427 ISBN-13(EAN): 9780387261423
Издательство: Springer
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Цена: 102480.00 T
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Описание: This book presents the contemporary statistical methods and theory of nonlinear time series analysis. The principal focus is on nonparametric and semiparametric techniques developed in the last decade. It covers the techniques for modelling in state-space, in frequency-domain as well as in time-domain. To reflect the integration of parametric and nonparametric methods in analyzing time series data, the book also presents an up-to-date exposure of some parametric nonlinear models, including ARCH/GARCH models and threshold models. A compact view on linear ARMA models is also provided. Data arising in real applications are used throughout to show how nonparametric approaches may help to reveal local structure in high-dimensional data. Important technical tools are also introduced. The book will be useful for graduate students, application-oriented time series analysts, and new and experienced researchers. It will have the value both within the statistical community and across a broad spectrum of other fields such as econometrics, empirical finance, population biology and ecology. The prerequisites are basic courses in probability and statistics. Jianqing Fan, coauthor of the highly regarded book Local Polynomial Modeling, is Professor of Statistics at the University of North Carolina at Chapel Hill and the Chinese University of Hong Kong. His published work on nonparametric modeling, nonlinear time series, financial econometrics, analysis of longitudinal data, model selection, wavelets and other aspects of methodological and theoretical statistics has been recognized with the Presidents' Award from the Committee of Presidents of Statistical Societies, the Hettleman Prize for Artistic and Scholarly Achievement from the University of North Carolina, and by his election as a fellow of the American Statistical Association and the Institute of Mathematical Statistics. Qiwei Yao is Professor of Statistics at the London School of Economics and Political Science. He is an elected member of the International Statistical Institute, and has served on the editorial boards for the Journal of the Royal Statistical Society (Series B) and the Australian and New Zealand Journal of Statistics.

Автор: Hardle, Wolfgang Karl Muller, Marlene Sperlich, Stefan Werwatz, Axel
Название: Nonparametric and semiparametric models
ISBN: 3642620760 ISBN-13(EAN): 9783642620768
Издательство: Springer
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Цена: 144410.00 T
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Описание: 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.