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Parametric and Nonparametric Inference from Record-Breaking Data, Gulati Sneh, Padgett William J.



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Цена: 8359р.
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Автор: Gulati Sneh, Padgett William J.
Название:  Parametric and Nonparametric Inference from Record-Breaking Data
Издательство: Springer
Классификация:
ISBN: 0387001387
ISBN-13(EAN): 9780387001388
Обложка/Формат: Paperback
Страницы: 121
Вес: 0.196 кг.
Дата издания: 17.02.2003
Серия: Lecture Notes in Statistics
Язык: English
Издание: 2003 ed.
Иллюстрации: 2 illustrations, black and white; viii, 117 p. 2 illus.
Размер: 23.37 x 15.75 x 0.81
Читательская аудитория: Professional & vocational
Ссылка на Издательство: Link
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Поставляется из: Германии
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.
Дополнительное описание: Формат: 235x155
Илюстрации: 1
Круг читателей: ResearchersNone
Ключевые слова:
Язык: eng
Оглавление: Introduction * Preliminaries and early work * Parametric inference * Nonparametric inference-genesis * Smooth function estimation * Bayesian models * Record models with trend





Автор: Hald Anders
Название: A History of Parametric Statistical Inference from Bernoulli to Fisher, 1713-1935
ISBN: 0387464085 ISBN-13(EAN): 9780387464084
Издательство: Springer
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Цена: 12539 р.
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Описание: This is a history of parametric statistical inference, written by one of the most important historians of statistics of the 20th century, Anders Hald. This book can be viewed as a follow-up to his two most recent books, although this current text is much more streamlined and contains new analysis of many ideas and developments. And unlike his other books, which were encyclopedic by nature, this book can be used for a course on the topic, the only prerequisites being a basic course in probability and statistics.The book is divided into five main sections:* Binomial statistical inference;* Statistical inference by inverse probability;* The central limit theorem and linear minimum variance estimation by Laplace and Gauss;* Error theory, skew distributions, correlation, sampling distributions;* The Fisherian Revolution, 1912-1935.Throughout each of the chapters, the author provides lively biographical sketches of many of the main characters, including Laplace, Gauss, Edgeworth, Fisher, and Karl Pearson. He also examines the roles played by DeMoivre, James Bernoulli, and Lagrange, and he provides an accessible exposition of the work of R.A. Fisher.This book will be of interest to statisticians, mathematicians, undergraduate and graduate students, and historians of science.

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.

Introduction to Nonparametric Regression

Автор: K. Takezawa
Название: Introduction to Nonparametric Regression
ISBN: 0471745839 ISBN-13(EAN): 9780471745839
Издательство: Wiley
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Цена: 17325 р.
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Описание: "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.

Applied Nonparametric Statistical Methods, Fourth Edition

Автор: Sprent
Название: Applied Nonparametric Statistical Methods, Fourth Edition
ISBN: 158488701X ISBN-13(EAN): 9781584887010
Издательство: Taylor&Francis
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Цена: 9008 р.
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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.

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.

Practical Nonparametric Statistics

Автор: Conover, W.J.
Название: Practical Nonparametric Statistics
ISBN: 0471160687 ISBN-13(EAN): 9780471160687
Издательство: Wiley
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Цена: 22248 р.
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Описание: This text aims to serve as a quick reference book offering instructions on how and when to use the most popular nonparametric procedures. It features procedures such as the Fisher Exact Test for two-by-two contingency tables, and the Mantel-Haenszel Test for combining several contingency tables.

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
Рейтинг:
Цена: 18809 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.

All of Nonparametric Statistics

Автор: Wasserman
Название: All of Nonparametric Statistics
ISBN: 0387251456 ISBN-13(EAN): 9780387251455
Издательство: Springer
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Цена: 13584 р.
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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
Рейтинг:
Цена: 8359 р.
Наличие на складе: Есть у поставщика 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

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: 9404 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.

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: 11146 р.
Наличие на складе: Нет в наличии.

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.

Nonparametric Functional Data Analysis

Автор: Ferraty
Название: Nonparametric Functional Data Analysis
ISBN: 0387303693 ISBN-13(EAN): 9780387303697
Издательство: Springer
Рейтинг:
Цена: 13584 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.


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