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Nonparametric Regression and Generalized Linear Models, Green



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Автор: Green
Название:  Nonparametric Regression and Generalized Linear Models
Издательство: Taylor&Francis
Классификация:
ISBN: 0412300400
ISBN-13(EAN): 9780412300400
Обложка/Формат: Hardback
Страницы: 184
Вес: 0.414 кг.
Дата издания: 01.05.1993
Серия: Chapman & Hall/CRC Monographs on Statistics & Applied Probability
Язык: English
Иллюстрации: Black & white illustrations
Размер: 162 x 232 x 16
Читательская аудитория: Tertiary education (us: college)
Ключевые слова: Statistical Computing, , Science
Основная тема: Statistical Theory & Methods
Подзаголовок: A roughness penalty approach
Ссылка на Издательство: Link
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Поставляется из: Англии
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.



Generalized Method of Moments

Автор: Hall, Alastair R.
Название: Generalized Method of Moments
ISBN: 0198775202 ISBN-13(EAN): 9780198775201
Издательство: Oxford Academ
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Цена: 7593 р.
Наличие на складе: Невозможна поставка.

Описание: This book has become one of the main statistical tools for the analysis of economic and financial data. Designed for both theoreticians and practitioners, this book provides a comprehensive treatment of GMM estimation and inference. All the main statistical results are discussed intuitively and proved formally, and all the inference techniques are illustrated using empirical examples in macroeconomics and finance. This book is the first to provide an intuitive introduction to the method combined with a unified treatment of GMM statistical theory and a survey of recent important developments in the field.
About the Series
Advanced Texts in Econometrics is a distinguished and rapidly expanding series in which leading econometricians assess recent developments in such areas as stochastic probability, panel and time series data analysis, modeling, and cointegration. In both hardback and affordable paperback, each volume explains the nature and applicability of a topic in greater depth than possible in introductory textbooks or single journal articles. Each definitive work is formatted to be as accessible and convenient for those who are not familiar with the detailed primary literature.

An Introduction to Generalized Linear Models, Third Edition

Название: An Introduction to Generalized Linear Models, Third Edition
ISBN: 1584889500 ISBN-13(EAN): 9781584889502
Издательство: Taylor&Francis
Рейтинг:
Цена: 5427 р.
Наличие на складе: Поставка под заказ.

Описание: Offers a cohesive framework for statistical modeling. Emphasizing numerical and graphical methods, this work enables readers to understand the unifying structure that underpins GLMs. It discusses common concepts and principles of advanced GLMs, including nominal and ordinal regression, survival analysis, and longitudinal analysis.

Generalized Additive Models: An Introduction with R

Автор: Wood, Simon
Название: Generalized Additive Models: An Introduction with R
ISBN: 1584884746 ISBN-13(EAN): 9781584884743
Издательство: Taylor&Francis
Рейтинг:
Цена: 8084 р.
Наличие на складе: Поставка под заказ.

Описание: An Introduction to Generalized Additive Models with R provides readers with a thorough understanding of the theory and practical applications of GAMs to enable informed use of these very flexible tools and other advanced related models. The author's approach is based on a framework of penalized regression splines, and he provides a gentle introduction through motivating chapters on linear and generalized linear models. The author uses the freely available R software throughout to explain the underlying theory and illustrate the practicalities of linear, generalized linear, and generalized additive models. The text is accompanied by a supporting Web site that contains R code and the datasets used in the book.

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.

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.

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.

Applied regression analysis and generalized linear models

Автор: Fox, Dr. John (mcmaster University, Hamilton, Onta
Название: Applied regression analysis and generalized linear models
ISBN: 0761930426 ISBN-13(EAN): 9780761930426
Издательство: Sage Publications
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Цена: 7017 р.
Наличие на складе: Поставка под заказ.

Описание: Gives coverage to regression models such as: generalized linear models; limited-dependent-variable-models; mixed models and Cox regression, among other methods.

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.

Applied Regression Analysis and Generalized Linear Models

Автор: Fox John
Название: Applied Regression Analysis and Generalized Linear Models
ISBN: 1452205663 ISBN-13(EAN): 9781452205663
Издательство: Sage Publications
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Цена: 13229 р.
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Описание: Combining a modern, data-analytic perspective with a focus on applications in the social sciences, the Third Edition of Applied Regression Analysis and Generalized Linear Models provides in-depth coverage of regression analysis, generalized linear models, and closely related methods, such as bootstrapping and missing data. Updated throughout, this Third Edition includes new chapters on mixed-effects models for hierarchical and longitudinal data. Although the text is largely accessible to readers with a modest background in statistics and mathematics, author John Fox also presents more advanced material in optional sections and chapters throughout the book.

Nonparametric Simple Regression

Автор: Fox J
Название: Nonparametric Simple Regression
ISBN: 0761915850 ISBN-13(EAN): 9780761915850
Издательство: Sage Publications
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Цена: 2070 р.
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Описание: John Fox introduces readers to the techniques of kernel estimation, additive nonparametric regression, and the ways nonparametric regression can be employed to select transformations of the data preceding a linear least-squares fit.

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: 17241 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.

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.


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