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Nonlinear Time Series / Nonparametric and Parametric Methods, Fan Jianqing, Yao Qiwei

Автор: Fan Jianqing, Yao Qiwei
Название:  Nonlinear Time Series / Nonparametric and Parametric Methods   (Нелинейный временной ряд / непараметрические и параметрические методы)
Издательство: Springer
Классификация:
Эконометрика
Вероятность и статистика
Прикладная математика

ISBN: 0387261427
ISBN-13(EAN): 9780387261423
ISBN: 0-387-26142-7
ISBN-13(EAN): 978-0-387-26142-3
Обложка/Формат: Paperback
Страницы: 571
Вес: 0.797 кг.
Дата издания: 07.09.2005
Серия: Springer Series in Statistics
Издание: 1st ed. 2003. 2nd pr
Иллюстрации: Illustrations
Размер: 156 x 234 x 29
Читательская аудитория: Postgraduate, research & scholarly
Подзаголовок: Nonparametric and parametric methods
Ссылка на Издательство: Link
Рейтинг:
Поставляется из: Германии
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.
Дополнительное описание: Формат: 235x155
Круг читателей: Researchers, graduate students
Ключевые слова:
Язык: eng
Издание: 1st ed. 2003. 2nd printin
Оглавление: Introduction.- Characteristics of Time Series.- ARMA Modeling and Forecasting.- Parametric Nonlinear Time Series Models.- Nonparametric Density Estimation.- Smoothing in Time Series.- Spectral Density Estimation and Its Applications.- Nonparametric Models.- Model Validation.- Nonlinear Prediction.



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Applied Nonparametric Econometrics

Автор: Henderson
Название: Applied Nonparametric Econometrics
ISBN: 0521279682 ISBN-13(EAN): 9780521279680
Издательство: Cambridge Academ
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Цена: 2886 р.
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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.

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The Oxford Handbook of Applied Nonparametric and Semiparametric Econometrics and Statistics

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
Рейтинг:
Цена: 9778 р.
Наличие на складе: Поставка под заказ.

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.

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Nonparametric Methods in Change Point Problems

Автор: Brodsky, E., Darkhovsky, B.S.
Название: Nonparametric Methods in Change Point Problems
ISBN: 0792321227 ISBN-13(EAN): 9780792321224
Издательство: Springer
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Цена: 7837 р.
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Описание: This volume deals with non-parametric methods of change point (disorder) detection in random processes and fields. A systematic account is given of up-to-date developments in this rapidly evolving branch of statistics.

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Advanced Linear Modeling / Multivariate, Time Series, and Spatial Data; Nonparametric Regression and Response Surface Maximization

Автор: Christensen Ronald
Название: Advanced Linear Modeling / Multivariate, Time Series, and Spatial Data; Nonparametric Regression and Response Surface Maximization
ISBN: 0387952969 ISBN-13(EAN): 9780387952963
Издательство: Springer
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Цена: 8246 р.
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Описание: This book introduces several topics related to linear model theory: multivariate linear models, discriminant analysis, principal components, factor analysis, time series in both the frequency and time domains, and spatial data analysis. The second edition adds new material on nonparametric regression, response surface maximization, and longitudinal models. The book provides a unified approach to these disparate subject and serves as a self-contained companion volume to the author's Plane Answers to Complex Questions: The Theory of Linear Models. Ronald Christensen is Professor of Statistics at the University of New Mexico. He is well known for his work on the theory and application of linear models having linear structure. He is the author of numerous technical articles and several books and he is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics. Also Available: Christensen, Ronald. Plane Answers to Complex Questions: The Theory of Linear Models, Second Edition (1996). New York: Springer-Verlag New York, Inc. Christensen, Ronald. Log-Linear Models and Logistic Regression, Second Edition (1997). New York: Springer-Verlag New York, Inc.

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Applied Nonparametric Statistical Methods, Fourth Edition

Автор: Sprent
Название: Applied Nonparametric Statistical Methods, Fourth Edition
ISBN: 158488701X ISBN-13(EAN): 9781584887010
Издательство: Taylor&Francis
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Цена: 6170 р.
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Описание: While preserving the clear, accessible style of previous editions, this fourth edition reflects the latest developments in computer-intensive methods that deal with intractable analytical problems and unwieldy data sets. This edition summarizes relevant general statistical concepts and introduces basic ideas of nonparametric or distribution-free methods. Designed experiments, including those with factorial treatment structures, are now the focus of an entire chapter. The book also expands coverage on the analysis of survival data and the bootstrap method. The new final chapter focuses on important modern developments. With numerous exercises, the text offers the student edition of StatXact at a discounted price.

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Semiparametric and Nonparametric Methods in Econometrics

Автор: Horowitz
Название: Semiparametric and Nonparametric Methods in Econometrics
ISBN: 0387928693 ISBN-13(EAN): 9780387928692
Издательство: Springer
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Цена: 11549 р.
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Описание: Presents the ideas underlying a variety of nonparametric and semiparametric methods. This book emphasizes ideas instead of technical details and provides an intuitive exposition. It is suitable for graduate students and applied researchers who are familiar with econometric theory.

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Robust Nonparametric Statistical Methods, Second Edition

Автор: Hettmansperger
Название: Robust Nonparametric Statistical Methods, Second Edition
ISBN: 1439809089 ISBN-13(EAN): 9781439809082
Издательство: Taylor&Francis
Рейтинг:
Цена: 8415 р.
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Описание: Presenting an extensive set of tools and methods for data analysis, this second edition includes more models and methods and significantly extends the possible analyses based on ranks. It contains a new section on rank procedures for nonlinear models, a new chapter on models with dependent error structure, and new material on the development of computationally efficient affine invariant/equivariant sign methods based on transform-retransform techniques in multivariate models. The authors illustrate the methods using many real-world examples and R. Information about the data sets and R packages can be found at www.crcpress.com

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Multivariate Nonparametric Methods with R

Автор: Oja Hannu
Название: Multivariate Nonparametric Methods with R
ISBN: 1441904670 ISBN-13(EAN): 9781441904676
Издательство: Springer
Рейтинг:
Цена: 10724 р.
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Описание: Offers a fresh, fairly efficient, and robust alternative to analyzing multivariate data. This monograph provides an overview of the theory of multivariate nonparametric methods based on spatial signs and ranks. It uses marginal signs and ranks and different type of L1 norm.

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Nonparametric Methods in Statistics with SAS Applications

Автор: Korosteleva
Название: Nonparametric Methods in Statistics with SAS Applications
ISBN: 1466580623 ISBN-13(EAN): 9781466580626
Издательство: Taylor&Francis
Рейтинг:
Цена: 4300 р.
Наличие на складе: Поставка под заказ.

Описание: This classroom-tested book teaches students how to apply nonparametric techniques to statistical data. It starts with the tests of hypotheses and moves on to regression modeling, time-to-event analysis, density estimation, and resampling methods. Along with exercises at the end of each chapter, the text includes various examples from psychology, education, clinical trials, and other areas. Complete SAS codes for all examples are given in the text. Large data sets for the exercises are available on the author’s website.

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Nonparametric Statistical Methods

Автор: Hollander Myles
Название: Nonparametric Statistical Methods
ISBN: 0470387378 ISBN-13(EAN): 9780470387375
Издательство: Wiley
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Цена: 9911 р.
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Описание: Written by leading statisticians, this new edition has been completely updated to include additional modern topics and procedures, more real-world data sets, and more problems from real-life situations.

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Robust Rank-Based and Nonparametric Methods

Автор: Liu
Название: Robust Rank-Based and Nonparametric Methods
ISBN: 3319390635 ISBN-13(EAN): 9783319390635
Издательство: Springer
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Цена: 9074 р.
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Описание: The contributors to this volume include many of the distinguished researchers in this area. Many of these scholars have collaborated with Joseph McKean to develop underlying theory for these methods, obtain small sample corrections, and develop efficient algorithms for their computation. The papers cover the scope of the area, including robust nonparametric rank-based procedures through Bayesian and big data rank-based analyses. Areas of application include biostatistics and spatial areas. Over the last 30 years, robust rank-based and nonparametric methods have developed considerably. These procedures generalize traditional Wilcoxon-type methods for one- and two-sample location problems. Research into these procedures has culminated in complete analyses for many of the models used in practice including linear, generalized linear, mixed, and nonlinear models. Settings are both multivariate and univariate. With the development of R packages in these areas, computation of these procedures is easily shared with readers and implemented. This book is developed from the International Conference on Robust Rank-Based and Nonparametric Methods, held at Western Michigan University in April 2015.

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Nonparametric Goodness-of-Fit Testing Under Gaussian Models

Автор: Ingster Yuri, Suslina I.A.
Название: Nonparametric Goodness-of-Fit Testing Under Gaussian Models
ISBN: 0387955313 ISBN-13(EAN): 9780387955315
Издательство: Springer
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Цена: 13612 р.
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Описание: There are two main problems in statistics, estimation theory and hypothesis testing. For the classical finite-parametric case, these problems were studied in parallel. On the other hand, many statistical problems are not parametric in the classical sense; the objects of estimation or testing arefunctions, images, and so on. These can be treated as unknown infinite-dimensional parameters that belongto specific functional sets. This approach to nonparametric estimation under asymptotically minimax setting was started in the 1960s-1970s and was developed very intensively for wide classes of functional sets and loss functions.Nonparametric estimation problems have generated a large literature. On the other hand, nonparametrichypotheses testing problems have not drawn comparable attention in the statistical literature. In this book, the authors develop a modern theory of nonparametric goodness-of-fit testing. The presentation is based on an asymptotic version of the minimax approach. The key element of the theory isthe method of constructing of asymptotically least favorable priors for a wide enough class of nonparametric hypothesis testing problems. These provide methods for the construction of asymptotically optimal, rate optimal, and optimal adaptive test procedures. The book is addressed to mathematical statisticians who are interesting in the theory of nonparametricstatistical inference. It will be of interest to specialists who are dealing with applied nonparametric statistical problems in signal detection and transmission, and technical and mother fields. The material is suitable for graduate courses on mathematical statistics. The book assumes familiarity with probability theory.

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