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Bayesian Nonparametrics, Ghosh J.K., Ramamoorthi R.V.


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Цена: 15427р.
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Склад Англия: 245 шт.  Склад Америка: 101 шт.  
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Автор: Ghosh J.K., Ramamoorthi R.V.
Название:  Bayesian Nonparametrics   (Дж. К. Грош, Р. В. Рамамурти: Байесовские непараметрические расчеты)
Издательство: Springer
Классификация:
Вероятность и статистика

ISBN: 0387955372
ISBN-13(EAN): 9780387955377
ISBN: 0-387-95537-2
ISBN-13(EAN): 978-0-387-95537-7
Обложка/Формат: Hardback
Страницы: 324
Вес: 1.39 кг.
Дата издания: 28.04.2003
Серия: Springer Series in Statistics
Язык: ENG
Иллюстрации: 4 black & white illustrations, 4 black & white lin
Размер: 24.33 x 15.60 x 2.06 cm
Читательская аудитория: Postgraduate, research & scholarly
Ссылка на Издательство: Link
Рейтинг:
Поставляется из: Германии
Описание: Bayesian nonparametrics has grown tremendously in the last three decades, especially in the last few years. This book is the first systematic treatment of Bayesian nonparametric methods and the theory behind them. While the book is of special interest to Bayesians, it will also appeal to statisticians in general because Bayesian nonparametrics offers a whole continuous spectrum of robust alternatives to purely parametric and purely nonparametric methods of classical statistics. The book is primarily aimed at graduate students and can be used as the text for a graduate course in Bayesian nonparametrics. Though the emphasis of the book is on nonparametrics, there is a substantial chapter onJayanta Ghosh has been Director and Jawaharlal Nehru Professor at the Indian Statistical Institute and President of the International Statistical Institute. He is currently professor of statistics at Purdue University. He has been editor of Sankhya and served on the editorial boards of several journals including the Annals of Statistics. Apart from Bayesian analysis, his interests include asymptotics, stochastic modeling, high dimensional model selection, reliability and survival analysis and bioinformatics.R.V. Ramamoorthi is professor at the Department of Statistics and Probability at Michigan State University. He has published papers in the areas of sufficiency invariance, comparison of experiments, nonparametric survival analysis and Bayesian analysis. In addition to Bayesian nonparametrics, he is currently interested in Bayesian networks and graphical models. He is on the editorial board of Sankhya.
Дополнительное описание: Формат: 235x155
Илюстрации: 4
Круг читателей: Graduate students, researchers
Ключевые слова:
Язык: eng





Bayesian Nonparametric Data Analysis

Автор: Muller, P., Quintana, F.A., Jara, A., Hanson, T.
Название: Bayesian Nonparametric Data Analysis
ISBN: 3319189670 ISBN-13(EAN): 9783319189673
Издательство: Springer
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Цена: 7947 р.
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Описание: This book reviews nonparametric Bayesian methods and models that have proven useful in the context of data analysis. In selecting specific nonparametric models, simpler and more traditional models are favored over specialized ones.

Fundamentals of Nonparametric Bayesian Inference

Автор: Ghosal, Subhashis.
Название: Fundamentals of Nonparametric Bayesian Inference
ISBN: 0521878268 ISBN-13(EAN): 9780521878265
Издательство: Cambridge Academ
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Цена: 6764 р.
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Описание: Explosive growth in computing power has made Bayesian methods for infinite-dimensional models - Bayesian nonparametrics - a nearly universal framework for inference, finding practical use in numerous subject areas. Written by leading researchers, this authoritative text draws on theoretical advances of the past twenty years to synthesize all aspects of Bayesian nonparametrics, from prior construction to computation and large sample behavior of posteriors. Because understanding the behavior of posteriors is critical to selecting priors that work, the large sample theory is developed systematically, illustrated by various examples of model and prior combinations. Precise sufficient conditions are given, with complete proofs, that ensure desirable posterior properties and behavior. Each chapter ends with historical notes and numerous exercises to deepen and consolidate the reader's understanding, making the book valuable for both graduate students and researchers in statistics and machine learning, as well as in application areas such as econometrics and biostatistics.

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: 14492 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: 15427 р.
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Описание: Gives an introduction to nonparametric curve estimation theory.

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: 18232 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.

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: 15427 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: 18214 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.

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: 8414 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.

Nonlinear Time Series / Nonparametric and Parametric Methods

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: 10284 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

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.

Nonparametric Functional Data Analysis

Автор: Ferraty
Название: Nonparametric Functional Data Analysis
ISBN: 0387303693 ISBN-13(EAN): 9780387303697
Издательство: Springer
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Цена: 12154 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: Modern apparatuses allow us to collect samples of functional data, mainly curves but also images. On the other hand, nonparametric statistics produces useful tools for standard data exploration. This book links these two fields of modern statistics by explaining how functional data can be studied through parameter-free statistical ideas.

Bayesian Nonparametrics via Neural Networks

Автор: Herbert K. H. Lee
Название: Bayesian Nonparametrics via Neural Networks
ISBN: 0898715636 ISBN-13(EAN): 9780898715637
Издательство: Eurospan
Рейтинг:
Цена: 6327 р.
Наличие на складе: Нет в наличии.

Описание: Bayesian Nonparametrics via Neural Networks is the first book to focus on neural networks in the context of nonparametric regression and classification, working within the Bayesian paradigm. It discusses neural networks in a statistical context, an approach in contrast to existing books, which tend to treat neural networks as a machine learning algorithm instead of a statistical model. Once this underlying statistical model is recognized, other standard statistical techniques can be applied to improve the model. The Bayesian approach allows better accounting for uncertainty. This book covers uncertainty in model choice and ways to deal with this issue, exploring ideas from statistics and machine learning. An analysis on the choice of prior and new noninformative priors is included, along with a substantial literature review. Written for statisticians using statistical terminology, this book will lead statisticians to an increased understanding of the neural network model and its applicability to real-world problems.

Bayesian Nonparametrics

Автор: Hjort
Название: Bayesian Nonparametrics
ISBN: 0521513464 ISBN-13(EAN): 9780521513463
Издательство: Cambridge Academ
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Цена: 5203 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: Bayesian nonparametrics works - theoretically, computationally. The theory provides highly flexible models whose complexity grows appropriately with the amount of data. Computational issues, though challenging, are no longer intractable. All that is needed is an entry point: this intelligent book is the perfect guide to what can seem a forbidding landscape. Tutorial chapters by Ghosal, Lijoi and Pr?nster, Teh and Jordan, and Dunson advance from theory, to basic models and hierarchical modeling, to applications and implementation, particularly in computer science and biostatistics. These are complemented by companion chapters by the editors and Griffin and Quintana, providing additional models, examining computational issues, identifying future growth areas, and giving links to related topics. This coherent text gives ready access both to underlying principles and to state-of-the-art practice. Specific examples are drawn from information retrieval, NLP, machine vision, computational biology, biostatistics, and bioinformatics.


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