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Nonparametric and Semiparametric Models, H?rdle



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Цена: 16196р.
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Автор: H?rdle
Название:  Nonparametric and Semiparametric Models
Издательство: Springer
Классификация:
ISBN: 3540207228
ISBN-13(EAN): 9783540207221
Обложка/Формат: Hardback
Страницы: 328
Вес: 0.632 кг.
Дата издания: 22.03.2004
Серия: Statistics for Business/Economics/Mathematical Finance/Insurance / Springer Series in Statistics
Язык: English
Издание: 2005. corr. 2nd
Иллюстрации: Illustrations
Размер: 244 x 168 x 25
Читательская аудитория: Postgraduate, research & scholarly
Ссылка на Издательство: Link
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Поставляется из: Германии
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.



Semiparametric Regression for the Social Sciences

Название: Semiparametric Regression for the Social Sciences
ISBN: 0470319917 ISBN-13(EAN): 9780470319918
Издательство: Wiley
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Цена: 7017 р.
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Описание: An introductory guide to smoothing techniques, semiparametric estimators, and their related methods, this book describes the methodology via a selection of carefully explained examples and data sets. It also demonstrates the potential of these techniques using detailed empirical examples drawn from the social and political sciences.

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 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 Monte Carlo Tests and Their Applications

Автор: Zhu Lixing
Название: Nonparametric Monte Carlo Tests and Their Applications
ISBN: 0387250387 ISBN-13(EAN): 9780387250380
Издательство: Springer
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Цена: 8359 р.
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Описание: A fundamental issue in statistical analysis is testing the fit of a particular probability model to a set of observed data. Monte Carlo approximation to the null distribution of the test provides a convenient and powerful means of testing model fit. Nonparametric Monte Carlo Tests and Their Applications proposes a new Monte Carlo-based methodology to construct this type of approximation when the model is semistructured. When there are no nuisance parameters to be estimated, the nonparametric Monte Carlo test can exactly maintain the significance level, and when nuisance parameters exist, this method can allow the test to asymptotically maintain the level. The author addresses both applied and theoretical aspects of nonparametric Monte Carlo tests. The new methodology has been used for model checking in many fields of statistics, such as multivariate distribution theory, parametric and semiparametric regression models, multivariate regression models, varying-coefficient models with longitudinal data, heteroscedasticity, and homogeneity of covariance matrices. This book will be of interest to both practitioners and researchers investigating goodness-of-fit tests and resampling approximations.Every chapter of the book includes algorithms, simulations, and theoretical deductions. The prerequisites for a full appreciation of the book are a modest knowledge of mathematical statistics and limit theorems in probability/empirical process theory. The less mathematically sophisticated reader will find Chapters 1, 2 and 6 to be a comprehensible introduction on how and where the new method can apply and the rest of the book to be a valuable reference for Monte Carlo test approximation and goodness-of-fit tests.Lixing Zhu is Associate Professor of Statistics at the University of Hong Kong. He is a winner of the Humboldt Research Award at Alexander-von Humboldt Foundation of Germany and an elected Fellow of the Institute of Mathematical Statistics.From the reviews:"These lecture notes discuss several topics in goodness-of-fit testing, a classical area in statistical analysis. … The mathematical part contains detailed proofs of the theoretical results. Simulation studies illustrate the quality of the Monte Carlo approximation. … this book constitutes a recommendable contribution to an active area of current research." Winfried Stute for Mathematical Reviews, Issue 2006"...Overall, this is an interesting book, which gives a nice introduction to this new and specific field of resampling methods." Dongsheng Tu for Biometrics, September 2006

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.

Efficient and Adaptive Estimation for Semiparametric Models

Автор: Bickel
Название: Efficient and Adaptive Estimation for Semiparametric Models
ISBN: 0387984739 ISBN-13(EAN): 9780387984735
Издательство: Springer
Цена: 11494 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: "Efficient and Adaptive Estimation for Semiparametric Models".

Parametric and Semiparametric Models with Applications to Reliability, Survival Analysis, and Quality of Life

Автор: Nikulin
Название: Parametric and Semiparametric Models with Applications to Reliability, Survival Analysis, and Quality of Life
ISBN: 081763231X ISBN-13(EAN): 9780817632311
Издательство: Springer
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Цена: 18809 р.
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Описание: Parametric and semiparametric models are tools with a wide range of applications to reliability, survival analysis, and quality of life. This self-contained volume examines these tools in survey articles written by experts currently working on the development and evaluation of models and methods. While a number of chapters deal with general theory, several explore more specific connections and recent results in "real-world" reliability theory, survival analysis, and related fields.

Semiparametric Regression

Автор: David Ruppert
Название: Semiparametric Regression
ISBN: 0521785162 ISBN-13(EAN): 9780521785167
Издательство: Cambridge Academ
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Цена: 4945 р.
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Описание: Semiparametric regression is concerned with the flexible incorporation of non-linear functional relationships in regression analyses. Any application area that benefits from regression analysis can also benefit from semiparametric regression. Assuming only a basic familiarity with ordinary parametric regression, this user-friendly book explains the techniques and benefits of semiparametric regression in a concise and modular fashion. The authors make liberal use of graphics and examples plus case studies taken from environmental, financial, and other applications. They include practical advice on implementation and pointers to relevant software. The book is suitable as a textbook for students with little background in regression as well as a reference book for statistically oriented scientists such as biostatisticians, econometricians, quantitative social scientists, epidemiologists, with a good working knowledge of regression and the desire to begin using more flexible semiparametric models. Even experts on semiparametric regression should find something new here.

Semiparametric Theory and Missing Data

Автор: Tsiatis
Название: Semiparametric Theory and Missing Data
ISBN: 0387324488 ISBN-13(EAN): 9780387324487
Издательство: Springer
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Цена: 15674 р.
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Описание: This book summarizes current knowledge of the theory of estimation for semiparametric models with missing data, applying modern methods to missing, censored, and coarsened data with the goal of deriving estimators that are as robust and efficient as possible.

Semiparametric Methods in Econometrics

Автор: Horowitz
Название: Semiparametric Methods in Econometrics
ISBN: 0387984771 ISBN-13(EAN): 9780387984773
Издательство: Springer
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Цена: 14629 р.
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Описание: During the 1980s and 1990s, much research has been carried out on semiparametric econometric models that are relevant to empirical economics. This text synthesizes the results that have been achieved for five classes of these models.

Introduction to empirical processes and semiparametric inference

Автор: Kosorok, Michael R.
Название: Introduction to empirical processes and semiparametric inference
ISBN: 0387749772 ISBN-13(EAN): 9780387749778
Издательство: Springer
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Цена: 15674 р.
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Описание: Presents an introduction to empirical processes and semi parametric inference. This book is suitable for statisticians, biostatisticians, and other researchers with a background in mathematical statistics who have an interest in learning about and doing research in empirical processes and semi parametric inference.

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.


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