Nonparametric Function Estimation, Modeling, and Simulation, James R. Thompson, Richard A. Tapia

Автор: Alexandre B. Tsybakov
Название: Introduction to Nonparametric Estimation
ISBN: 0387790519 ISBN-13(EAN): 9780387790510
Издательство: Springer
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Цена: 102480.00 T
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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.

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: 79190.00 T
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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.

Автор: Pao-Liu Chow; Boris S. Mordukhovich; G. George Yin
Название: Topics in Stochastic Analysis and Nonparametric Estimation
ISBN: 1441925813 ISBN-13(EAN): 9781441925817
Издательство: Springer
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Цена: 97820.00 T
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Описание: Khasminskii, on his seventy-fifth birthday, for his contributions to stochastic processes and nonparametric estimation theory an IMA participating institution conference entitled "Conference on Asymptotic Analysis in Stochastic Processes, Nonparametric Estimation, and Related Problems" was held.

Автор: Efromovich, Sam (ut Dallas, Richardson, Tx)
Название: Missing and modified data in nonparametric estimation
ISBN: 1138054887 ISBN-13(EAN): 9781138054882
Издательство: Taylor&Francis
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Цена: 100030.00 T
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Описание: This book presents a systematic and unified approach for modern nonparametric treatment of missing and modified data via examples of density and hazard rate estimation, nonparametric regression, filtering signals, and time series analysis. All basic types of missing at random and not at random, biasing, truncation, censoring, and measurement errors are discussed, and their treatment is explained. Ten chapters of the book cover basic cases of direct data, biased data, nondestructive and destructive missing, survival data modified by truncation and censoring, missing survival data, stationary and nonstationary time series and processes, and ill-posed modifications. The coverage is suitable for self-study or a one-semester course for graduate students with a prerequisite of a standard course in introductory probability. Exercises of various levels of difficulty will be helpful for the instructor and self-study. The book is primarily about practically important small samples. It explains when consistent estimation is possible, and why in some cases missing data should be ignored and why others must be considered. If missing or data modification makes consistent estimation impossible, then the author explains what type of action is needed to restore the lost information. The book contains more than a hundred figures with simulated data that explain virtually every setting, claim, and development. The companion R software package allows the reader to verify, reproduce and modify every simulation and used estimators. This makes the material fully transparent and allows one to study it interactively. Sam Efromovich is the Endowed Professor of Mathematical Sciences and the Head of the Actuarial Program at the University of Texas at Dallas. He is well known for his work on the theory and application of nonparametric curve estimation and is the author of Nonparametric Curve Estimation: Methods, Theory, and Applications. Professor Sam Efromovich is a Fellow of the Institute of Mathematical Statistics and the American Statistical Association.

Автор: Alexandre B. Tsybakov
Название: Introduction to Nonparametric Estimation
ISBN: 1441927093 ISBN-13(EAN): 9781441927095
Издательство: Springer
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Цена: 102480.00 T
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Описание: Developed from lecture notes and ready to be used for a course on the graduate level, this concise text aims to introduce the fundamental concepts of nonparametric estimation theory while maintaining the exposition suitable for a first approach in the field.

Автор: Sam Efromovich
Название: Nonparametric Curve Estimation
ISBN: 1475773013 ISBN-13(EAN): 9781475773019
Издательство: Springer
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Цена: 93160.00 T
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Описание: This book gives a systematic, comprehensive, and unified account of modern nonparametric statistics of density estimation, nonparametric regression, filtering signals, and time series analysis.

Автор: Groeneboom
Название: Nonparametric Estimation under Shape Constraints
ISBN: 0521864011 ISBN-13(EAN): 9780521864015
Издательство: Cambridge Academ
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Цена: 79200.00 T
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Описание: This book treats the latest developments in the theory of order-restricted inference, with special attention to nonparametric methods and algorithmic aspects. Each chapter ends with a set of exercises of varying difficulty. The theory is illustrated with the analysis of real-life data, which are mostly medical in nature.

Автор: M. Luz G?miz; K. B. Kulasekera; Nikolaos Limnios;
Название: Applied Nonparametric Statistics in Reliability
ISBN: 1447126343 ISBN-13(EAN): 9781447126348
Издательство: Springer
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Цена: 158380.00 T
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Описание: This volume focuses on the latest statistical methods used to estimate the performance measures of reliability systems that operate under different conditions. It includes numerous techniques such as nonparametric estimation and lifetime regression analysis.

Автор: Nadaraya
Название: Nonparametric Estimation of Probability Densities and Regression Curves
ISBN: 9027727570 ISBN-13(EAN): 9789027727572
Издательство: Springer
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Цена: 88500.00 T
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Описание: 'Et moi, ..., si. j'avail su comment en revenir. One service mathematics has rendered be human race. It has put common sense back jc n'y scrais point a1U: where it belongs, on the topmost sbelf next Jules Verne to \be dusty canister labelled 'discarded non- TIle series is divergent; therefore we may be sense'. able to do something with it Eric T. Bell O. Heaviside Mathematics is a tool for thought. A highly necessary tool in a world where both feedback and non- linearities abound. Similarly, all kinds of parts of mathematics serve as tools for other parts and for other sciences. Applying a simple rewriting rule to the quote on the right above one finds such statements as: 'One service topology has rendered mathematical physics .. .'; 'One service logic bas rendered com- puter science .. .'; 'One service category theory has rendered mathematics .. .'. All arguably true. And all statements obtainable this way form part of the raison d'etre of this series.

Автор: Gramacki
Название: Nonparametric Kernel Density Estimation and Its Computational Aspects
ISBN: 3319716875 ISBN-13(EAN): 9783319716879
Издательство: Springer
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Цена: 130430.00 T
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Описание: This book describes computational problems related to kernel density estimation (KDE)-one of the most important and widely used data smoothing techniques. This book is an attempt to remedy this. The book primarily addresses researchers and advanced graduate or postgraduate students who are interested in KDE and its computational aspects.

Автор: L?szl? Gy?rfi; Michael Kohler; Adam Krzyzak; Harro
Название: A Distribution-Free Theory of Nonparametric Regression
ISBN: 1441929983 ISBN-13(EAN): 9781441929983
Издательство: Springer
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Цена: 181670.00 T
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Описание: This book provides a systematic in-depth analysis of nonparametric regression with random design. It covers almost all known estimates. The emphasis is on distribution-free properties of the estimates.

Автор: Ghosal, Subhashis.
Название: Fundamentals of Nonparametric Bayesian Inference
ISBN: 0521878268 ISBN-13(EAN): 9780521878265
Издательство: Cambridge Academ
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Цена: 86590.00 T
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Описание: Written by top researchers, this self-contained text is the authoritative account of Bayesian nonparametrics, a nearly universal framework for inference in statistics and machine learning, with practical use in all areas of science, including economics and biostatistics. Appendices with prerequisites and numerous exercises support its use for graduate courses.