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Introduction to Nonparametric Regression, K. Takezawa



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Цена: 17325р.
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Автор: K. Takezawa
Название:  Introduction to Nonparametric Regression
Издательство: Wiley
Классификация:
ISBN: 0471745839
ISBN-13(EAN): 9780471745839
Обложка/Формат: Hardback
Страницы: 568
Вес: 0.872 кг.
Дата издания: 13.12.2005
Серия: Wiley series in probability and statistics
Язык: English
Иллюстрации: Illustrations
Размер: 239 x 167 x 36
Читательская аудитория: Professional & vocational
Ссылка на Издательство: Link
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Поставляется из: Англии
Описание: Introduction to Nonparametric Regression presents a complete but fundamental and readily accessible treatment of nonparametric regression, a subset of the larger area of nonparametric statistics. The explanations are presented in a user-friendly format and along with S-Plus and R subroutines in an effort to derive many of the real-world data and results. The overall theme of the book is to showcase the attractiveness and usefulness of nonparametric regression. In addition to discussing the usual kernel and spline methods, the book also briefly covers tree models.
Дополнительное описание: Кол-во стр.: 640
Дата издания: 2005





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: 9926 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.

Introduction to Nonparametric Estimation

Автор: Alexandre B. Tsybakov
Название: Introduction to Nonparametric Estimation
ISBN: 0387790519 ISBN-13(EAN): 9780387790510
Издательство: Springer
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Цена: 11494 р.
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Описание: Presents basic nonparametric regression and density estimators and analyzes their properties. This book covers minimax lower bounds, and develops advanced topics such as: Pinsker`s theorem, oracle inequalities, Stein shrinkage, and sharp minimax adaptivity.

Introduction to Nonparametric Statistics for the Biological Sciences Using R

Автор: MacFarland
Название: Introduction to Nonparametric Statistics for the Biological Sciences Using R
ISBN: 3319306332 ISBN-13(EAN): 9783319306339
Издательство: Springer
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Цена: 7000 р.
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Описание: This book contains a rich set of tools for nonparametric analyses, and the purpose of this supplemental text is to provide guidance to students and professional researchers onhow R is used for nonparametric data analysis in the biological sciences:To introduce when nonparametricapproaches to data analysis are appropriateTo introduce the leadingnonparametric tests commonly used in biostatistics and how R is used togenerate appropriate statistics for each testTo introduce common figurestypically associated with nonparametric data analysis and how R is used togenerate appropriate figures in support of each data setThe book focuses on how R is used todistinguish between data that could be classified as nonparametric as opposedto data that could be classified as parametric, with both approaches to data classification covered extensively.Following an introductory lesson on nonparametric statistics for the biological sciences, the book is organized into eight self-contained lessons on various analyses and tests using R to broadly compare differences between data sets and statistical approach.This supplemental text is intended for:Upper-level undergraduate and graduate students majoring in the biological sciences, specifically those in agriculture, biology, and health science - both students in lecture-type courses and also those engaged in research projects, such as a master's thesis or a doctoral dissertationAnd biological researchers at the professional level without a nonparametric statistics background but who regularly work with data more suitable to a nonparametric approach to data analysis

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: 9404 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.

A Distribution-Free Theory of Nonparametric Regression

Автор: Gy?rfi
Название: A Distribution-Free Theory of Nonparametric Regression
ISBN: 0387954414 ISBN-13(EAN): 9780387954417
Издательство: Springer
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Цена: 18809 р.
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Описание: Presents an approach to nonparametric regression with random design. This monograph is intended for graduate students and researchers in statistics, mathematics, computer science, and engineering.

Multivariate Nonparametric Regression and Visualization

Автор: Klemela Jussi
Название: Multivariate Nonparametric Regression and Visualization
ISBN: 0470384425 ISBN-13(EAN): 9780470384428
Издательство: Wiley
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Цена: 10505 р.
Наличие на складе: Поставка под заказ.

Описание: Covering classification and regression, Statistical Learning is the first of its kind to use visualization techniques to identify, test, and analyze classifiers for their most accurate exploration of data.

Nonparametric Regression and Generalized Linear Models

Автор: Green
Название: Nonparametric Regression and Generalized Linear Models
ISBN: 0412300400 ISBN-13(EAN): 9780412300400
Издательство: Taylor&Francis
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Цена: р.
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Описание: Nonparametric Regression and Generalized Linear Models focuses on the roughness penalty method of nonparametric smoothing and shows how this technique provides a unifying approach to a wide range of smoothing problems. The emphasis is methodological rather than theoretical, and the authors concentrate on statistical and computation issues. Real data examples are used to illustrate the various methods and to compare them with standard parametric approaches. The mathematical treatment is self-contained and depends mainly on simple linear algebra and calculus. This monograph will be useful both as a reference work for research and applied statisticians and as a text for graduate students.

Nonparametric and Semiparametric Models

Автор: H?rdle
Название: Nonparametric and Semiparametric Models
ISBN: 3540207228 ISBN-13(EAN): 9783540207221
Издательство: Springer
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Цена: 16196 р.
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Описание: The concept of nonparametric smoothing is a central idea in statistics that aims to simultaneously estimate and modes the underlying structure. This book aims to present the statistical and mathematical principles of smoothing with a focus on applicable techniques.

Nonparametric Curve Estimation

Автор: Efromovich
Название: Nonparametric Curve Estimation
ISBN: 0387987401 ISBN-13(EAN): 9780387987408
Издательство: Springer
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Цена: 17241 р.
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Описание: Gives an introduction to nonparametric curve estimation theory.

Practical Nonparametric and Semiparametric Bayesian Statistics

Автор: Dey
Название: Practical Nonparametric and Semiparametric Bayesian Statistics
ISBN: 0387985174 ISBN-13(EAN): 9780387985176
Издательство: Springer
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Цена: 17241 р.
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Описание: Nonparametric and semiparametric statistical methods are attractive to researchers in a large number of fields, including pharmaceuticals, medical and public health centers, financial institutions, and environmental monitoring centers. This volume presents both the theoretical and applied aspects of these methods.

Nonparametric Smoothing and Lack-of-Fit Tests

Автор: Hart
Название: Nonparametric Smoothing and Lack-of-Fit Tests
ISBN: 0387949801 ISBN-13(EAN): 9780387949802
Издательство: Springer
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Цена: 17241 р.
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Описание: A fundamental problem in statistical analysis is checking how well a particular probability model fits a set of observed data. In many settings, nonparametric smoothing methods provide a convenient and powerful means of testing model fit. Nonparametric Smoothing and Lack-of-Fit Tests explores the use of smoothing methods in testing the fit of parametric regression models.

The book reviews many of the existing methods for testing lack-of-fit and also proposes a number of new methods. Both applied and theoretical aspects of the model checking problems are addressed. As such, the book should be of interest to practitioners of statistics and researchers investigating either lack-of-fit tests or nonparametric smoothing ideas.

The first four chapters of the book are an introduction to the problem of estimating regression functions by nonparametric smoothers, primarily those of kernel and Fourier series type. This part of the book could be used as the foundation for a graduate level course on nonparametric function estimation. The prerequisites for a full appreciation of the book are a modest knowledge of calculus and some familiarity with the basics of mathematical statistics.

The less mathematically sophisticated reader will find Chapter 2 to be a comprehensible introduction to smoothing ideas and the rest of the book to be a valuable reference for both nonparametric function estimation and lack-of-fit tests. Jeffrey D. Hart is Pr fessor of Statistics at Texas A&M University.

He is an associate editor of the Journal of the American Statistical Association, an elected Fellow of the Institute of Mathematical Statistics, and winner of a distinguished teaching award at Texas A&M University.

Nonparametric inference

Название: Nonparametric inference
ISBN: 981270034X ISBN-13(EAN): 9789812700346
Издательство: World Scientific Publishing
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Цена: 17256 р.
Наличие на складе: Поставка под заказ.

Описание: This book provides a solid foundation on nonparametric inference for students taking a graduate course in nonparametric statistics and serves as an easily accessible source for researchers in the area. With the exception of some sections requiring familiarity with measure theory, readers with an advanced calculus background will be comfortable with the material.


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