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Principles of Nonparametric Learning, Györfi Laszlo

: 16191.
: .  .

: 30 2022
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: Györfi Laszlo
 Principles of Nonparametric Learning
ISBN: 9783211836880
: Springer
ISBN-10: 3211836888
/: Paperback
: 335
: 0.6 .
: 2002
: CISM International Centre for Mechanical Sciences
: English
: Softcover reprint of
: Biography
: 24.41 x 16.99 x 1.85
: Postgraduate, research & scholarly
: Link
: : 13
: Postgraduates in engineering, mathematics, computer science, researchers in universities, researchers in institutions
: eng
: Pattern classification and learning theory (G. Lugosi): A binary classification problem; Empirical risk minimization; Concentration inequalities; Vapnik-Chervonenkis theory; Minimax lower bounds; Complexity regularization; References.- Nonparametric regression estimation (L. Györfi, M. Kohler): Regression problem; Local averaging estimates; Consequences in pattern recognition; Definition of (penalized) least squares estimates; Consistency of least squares estimates; Consistency of penalized least squares estimates; Rate of convergence of least squares estimates; References.- Universal prediction (N. Cesa-Bianchi): Introduction; Potential-based forecasters; Convex loss functions; Exp-concave loss functions; Absolute loss; Logarithmic loss; Sequentioal pattern classification; References.- Learning-theoretic methods in vector quantization (T. Linder): Introduction; The fixed-rate quantization problem; Consistency of empirical design; Finite sample upper bounds; Minimax lower bounds; Fundamentals of variable-rate quantization; The Lagrangian formulation; Consistency of Lagrangian empirical design; Finite sample bounds in Lagrangian design; References.- Distribution and density estimation (L. Devroye, L. Györfi): Distribution estimation; The density estimation problem; The histogram density estimate; Choosing Between Two Densities; The Minimum Distance Estimate; The Kernel Density Estimate; Additive Estimates and Data Splitting; Bandwidth Selection for Kernel Estimates; References.- Programming applied to model identification (M. Sebag): Summary; Introduction; Artificial Evolution; Genetic Programming; Genetic Programming with Grammars; Discussion and Conclusion; References

Asymptotic Efficiency of Nonparametric Tests

: Nikitin
: Asymptotic Efficiency of Nonparametric Tests
ISBN: 0521115922 ISBN-13(EAN): 9780521115926
: Cambridge Academ
: 3970 .
  : .

: Making a substantiated choice of the most efficient statistical test is one of the basic problems of statistics. Asymptotic efficiency is an indispensable technique for comparing and ordering statistical tests in large samples. It is especially useful in nonparametric statistics where it is usually necessary to rely on heuristic tests. This monograph presents a unified treatment of the analysis and calculation of the asymptotic efficiencies of nonparametric tests. Powerful new methods are developed to evaluate explicitly different kinds of efficiencies. Of particular interest is the description of domains of the Bahadur local optimality and related characterisation problems based on recent research by the author. Other Russian results are also published here for the first time in English. Researchers, professionals and students in statistics will find this book invaluable.

Introduction to Nonparametric Estimation

: Alexandre B. Tsybakov
: Introduction to Nonparametric Estimation
ISBN: 0387790519 ISBN-13(EAN): 9780387790510
: Springer
: 12704 .
  : .

: 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.

Nonparametric and Semiparametric Models

: H?rdle
: Nonparametric and Semiparametric Models
ISBN: 3540207228 ISBN-13(EAN): 9783540207221
: Springer
: 17901 .
  : .

: 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 Techniques in Statistical Inference

: Puri
: Nonparametric Techniques in Statistical Inference
ISBN: 0521093058 ISBN-13(EAN): 9780521093057
: Cambridge Academ
: 6298 .
  : .

: Nonparametric techniques in statistics are those in which the data are ranked in order according to some particular characteristic. When applied to measurable characteristics, the use of such techniques often saves considerable calculation as compared with more formal methods, with only slight loss of accuracy. The field of nonparametric statistics is occupying an increasingly important role in statistical theory as well as in its applications. Nonparametric methods are mathematically elegant, and they also yield significantly improved performances in applications to agriculture, education, biometrics, medicine, communication, economics and industry.

Nonparametric Methods in Change Point Problems

: Brodsky, E., Darkhovsky, B.S.
: Nonparametric Methods in Change Point Problems
ISBN: 0792321227 ISBN-13(EAN): 9780792321224
: Springer
: 10971 .
  : .

: 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 Regression

: K. Takezawa
: Introduction to Nonparametric Regression
ISBN: 0471745839 ISBN-13(EAN): 9780471745839
: Wiley
: 20625 .
  : .

: "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.

Advanced Linear Modeling / Multivariate, Time Series, and Spatial Data; Nonparametric Regression and Response Surface Maximization

: Christensen Ronald
: Advanced Linear Modeling / Multivariate, Time Series, and Spatial Data; Nonparametric Regression and Response Surface Maximization
ISBN: 0387952969 ISBN-13(EAN): 9780387952963
: Springer
: 10394 .
  : .

: 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.

All of Nonparametric Statistics

: Wasserman
: All of Nonparametric Statistics
ISBN: 0387251456 ISBN-13(EAN): 9780387251455
: Springer
: 15014 .
  : .

: The goal of this text is to provide the reader with a single book where they can find a brief account of many, modern topics in nonparametric inference. The book is aimed at Master's level or Ph.D. level students in statistics, computer science, and engineering. It is also suitable for researchers who want to get up to speed quickly on modern nonparametric methods.This text covers a wide range of topics including: the bootstrap, the nonparametric delta method, nonparametric regression, density estimation, orthogonal function methods, minimax estimation, nonparametric confidence sets, and wavelets. The book has a mixture of methods and theory.From the reviews:"...The book is excellent." (Short Book Reviews of the ISI, June 2006)"Now we have All of Nonparametric Statistics … . the writing is excellent and the author is to be congratulated on the clarity achieved. … the book is excellent." (N.R. Draper, Short Book Reviews, Vol. 26 (1), 2006)"Overall, I enjoyed reading this book very much. I like Wasserman's intuitive explanations and careful insights into why one path or approach is taken over another. Most of all, I am impressed with the wealth of information on the subject of asymptotic nonparametric inferences." (Stergios B. Fotopoulos for Technometrics, Vol. 49, No. 1., February 2007)

Nonlinear Time Series / Nonparametric and Parametric Methods

: Fan Jianqing, Yao Qiwei
: Nonlinear Time Series / Nonparametric and Parametric Methods
ISBN: 0387261427 ISBN-13(EAN): 9780387261423
: Springer
: 12704 .
  : .

: 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

: Ferraty
: Nonparametric Functional Data Analysis
ISBN: 0387303693 ISBN-13(EAN): 9780387303697
: Springer
: 15014 .
  : .

: 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.

Nonparametric Monte Carlo Tests and Their Applications

: Zhu Lixing
: Nonparametric Monte Carlo Tests and Their Applications
ISBN: 0387250387 ISBN-13(EAN): 9780387250380
: Springer
: 9239 .
  : .

: 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

Nonparametric Goodness-of-Fit Testing Under Gaussian Models

: Ingster Yuri, Suslina I.A.
: Nonparametric Goodness-of-Fit Testing Under Gaussian Models
ISBN: 0387955313 ISBN-13(EAN): 9780387955315
: Springer
: 19056 .
  : .

: 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.

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