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Robust Rank-Based and Nonparametric Methods, Liu

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Цена: 12704р.
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Автор: Liu
Название:  Robust Rank-Based and Nonparametric Methods
ISBN: 9783319390635
Издательство: Springer
ISBN-10: 3319390635
Обложка/Формат: Hardback
Страницы: 277
Вес: 0.602 кг.
Дата издания: 2016
Серия: Springer proceedings in mathematics and statistics
Язык: English
Иллюстрации: 25 black & white illustrations, 6 colour illustrations, 30 colour tables, biography
Размер: 234 x 156 x 18
Читательская аудитория: General (us: trade)
Основная тема: Statistics
Подзаголовок: Michigan, USA, April 2015: Selected, Revised, and Extended Contributions
Ссылка на Издательство: Link
Поставляется из: Германии
Дополнительное описание:
1 Rank-Based Analysis of Linear Models and Beyond: A Review.- 2 Robust Signed-Rank Variable Selection in Linear Regression.- 3 Generalized Rank-Based Estimates for Linear Models with Cluster Correlated Data.- 4 Iterated Reweighted Rank-Based Estimate

Robust Nonparametric Statistical Methods, Second Edition

Автор: Hettmansperger
Название: Robust Nonparametric Statistical Methods, Second Edition
ISBN: 1439809089 ISBN-13(EAN): 9781439809082
Издательство: Taylor&Francis
Цена: 19250 р.
Наличие на складе: Поставка под заказ.

Описание: Presenting an extensive set of tools and methods for data analysis, this second edition includes more models and methods and significantly extends the possible analyses based on ranks. It contains a new section on rank procedures for nonlinear models, a new chapter on models with dependent error structure, and new material on the development of computationally efficient affine invariant/equivariant sign methods based on transform-retransform techniques in multivariate models. The authors illustrate the methods using many real-world examples and R. Information about the data sets and R packages can be found at www.crcpress.com

Parametric and Nonparametric Inference from Record-Breaking Data

Автор: Gulati Sneh, Padgett William J.
Название: Parametric and Nonparametric Inference from Record-Breaking Data
ISBN: 0387001387 ISBN-13(EAN): 9780387001388
Издательство: Springer
Цена: 9239 р.
Наличие на складе: Поставка под заказ.

Описание: This book provides a comprehensive look at statistical inference from record-breaking data in both parametric and nonparametric settings, including Bayesian inference. A unique feature is that it treats the area of nonparametric function estimation from such data in detail, gathering results on this topic to date in one accessible volume. Previous books on records have focused mainly on the probabilistic behavior of records, prediction of future records, and characterizations of the distributions of record values, addressing some inference methods only briefly. The main purpose of this book is to fill this void on general inference from record values.Statisticians, mathematicians, and engineers will find the book useful as a research reference and in learning about making inferences from record-breaking data. The book can also serve as part of a graduate-level statistics or mathematics course, complementing material on the probabilistic aspects of record values. For a basic understanding of the statistical concepts, a one-year graduate course in mathematical statistics provides sufficient background. For a detailed understanding of the convergence theory of the nonparametric function estimators, a course in measure theory or probability theory at the graduate level is useful. Sneh Gulati is Associate Professor of Statistics at Florida International University in Miami. She is currently an associate editor of the Journal of Statistical Computation and Simulation and has published several articles in statistics. Currently she serves as the president of the South Florida Chapter of the American Statistical Association and is also the chair of the Florida Commission of Hurricane Loss Projection Methodology.William J. Padgett is Professor of Statistics and was the founding Chair of the Department of Statistics at the University of South Carolina, Columbia. He has published numerous papers and articles, as well as three books, on statistics and probability and has served as an associate editor of eight statistical journals, including Technometrics, Lifetime Data Analysis, Naval Research Logistics, Journal of Statistical Computation and Simulation, and the Journal of Statistical Planning and Inference. He is a Fellow of both the American Statistical Association and the Institute of Mathematical Statistics and an elected ordinary member of the International Statistical Institute.

Principles of Nonparametric Learning

Автор: GyГ¶rfi Laszlo
Название: Principles of Nonparametric Learning
ISBN: 3211836888 ISBN-13(EAN): 9783211836880
Издательство: Springer
Цена: 16191 р.
Наличие на складе: Поставка под заказ.

Описание: The book provides systematic in-depth analysis of nonparametric learning. It covers the theoretical limits and the asymptotical optimal algorithms and estimates, such as pattern recognition, nonparametric regression estimation, universal prediction, vector quantization, distribution and density estimation and genetic programming.The book is mainly addressed to postgraduates in engineering, mathematics, computer science, and researchers in universities and research institutions.

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
Цена: 10971 р.
Наличие на складе: Поставка под заказ.

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.

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
Цена: 12704 р.
Наличие на складе: 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.

Applied Nonparametric Statistical Methods, Fourth Edition

Автор: Sprent
Название: Applied Nonparametric Statistical Methods, Fourth Edition
ISBN: 158488701X ISBN-13(EAN): 9781584887010
Издательство: Taylor&Francis
Цена: 12374 р.
Наличие на складе: Поставка под заказ.

Описание: While preserving the clear, accessible style of previous editions, this fourth edition reflects the latest developments in computer-intensive methods that deal with intractable analytical problems and unwieldy data sets. This edition summarizes relevant general statistical concepts and introduces basic ideas of nonparametric or distribution-free methods. Designed experiments, including those with factorial treatment structures, are now the focus of an entire chapter. The book also expands coverage on the analysis of survival data and the bootstrap method. The new final chapter focuses on important modern developments. With numerous exercises, the text offers the student edition of StatXact at a discounted price.

Multivariate Nonparametric Methods with R

Автор: Oja Hannu
Название: Multivariate Nonparametric Methods with R
ISBN: 1441904670 ISBN-13(EAN): 9781441904676
Издательство: Springer
Цена: 15014 р.
Наличие на складе: Поставка под заказ.

Описание: Offers a fresh, fairly efficient, and robust alternative to analyzing multivariate data. This monograph provides an overview of the theory of multivariate nonparametric methods based on spatial signs and ranks. It uses marginal signs and ranks and different type of L1 norm.

Nonparametric Methods in Statistics with SAS Applications

Автор: Korosteleva
Название: Nonparametric Methods in Statistics with SAS Applications
ISBN: 1466580623 ISBN-13(EAN): 9781466580626
Издательство: Taylor&Francis
Цена: 8661 р.
Наличие на складе: Невозможна поставка.

Описание: Designed for a graduate course in applied statistics, Nonparametric Methods in Statistics with SAS Applications teaches students how to apply nonparametric techniques to statistical data. It starts with the tests of hypotheses and moves on to regression modeling, time-to-event analysis, density estimation, and resampling methods. The text begins with classical nonparametric hypotheses testing, including the sign, Wilcoxon sign-rank and rank-sum, Ansari-Bradley, Kolmogorov-Smirnov, Friedman rank, Kruskal-Wallis H, Spearman rank correlation coefficient, and Fisher exact tests. It then discusses smoothing techniques (loess and thin-plate splines) for classical nonparametric regression as well as binary logistic and Poisson models. The author also describes time-to-event nonparametric estimation methods, such as the Kaplan-Meier survival curve and Cox proportional hazards model, and presents histogram and kernel density estimation methods. The book concludes with the basics of jackknife and bootstrap interval estimation. Drawing on data sets from the author’s many consulting projects, this classroom-tested book includes various examples from psychology, education, clinical trials, and other areas. It also presents a set of exercises at the end of each chapter. All examples and exercises require the use of SAS 9.3 software. Complete SAS codes for all examples are given in the text. Large data sets for the exercises are available on the author’s website.

Nonparametric Statistical Methods

Автор: Hollander Myles
Название: Nonparametric Statistical Methods
ISBN: 0470387378 ISBN-13(EAN): 9780470387375
Издательство: Wiley
Цена: 15256 р.
Наличие на складе: Поставка под заказ.

Описание: Written by leading statisticians, this new edition has been completely updated to include additional modern topics and procedures, more real-world data sets, and more problems from real-life situations.

Nonparametric Analysis of Longitudinal Data in Factorial Experiments

Автор: Edgar Brunner
Название: Nonparametric Analysis of Longitudinal Data in Factorial Experiments
ISBN: 047144166X ISBN-13(EAN): 9780471441663
Издательство: Wiley
Цена: 13269 р.
Наличие на складе: Поставка под заказ.

Описание: This title provides a unified approach for the analysis of factorial designs involving longitudinal data that is appropriate for metric data, count data, ordered categorical data, and dichotomous data.

All of Nonparametric Statistics

Автор: Wasserman
Название: All of Nonparametric Statistics
ISBN: 0387251456 ISBN-13(EAN): 9780387251455
Издательство: Springer
Цена: 15014 р.
Наличие на складе: Поставка под заказ.

Описание: 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)

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
Цена: 9239 р.
Наличие на складе: Поставка под заказ.

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

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