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Monday, July 27, 2020 | History

3 edition of Nonparametric sequential selection procedures found in the catalog.

Nonparametric sequential selection procedures

H. BuМ€ringer

Nonparametric sequential selection procedures

by H. BuМ€ringer

  • 316 Want to read
  • 6 Currently reading

Published by Birkhauser in Boston .
Written in English

    Subjects:
  • Ranking and selection (Statistics),
  • Sequential analysis.,
  • Nonparametric statistics.

  • Edition Notes

    Bibliography, p.

    StatementH. Büringer, H. Martin, K.-H. Schreiver.
    ContributionsMartin, Herbert., Schreiver, K.-H.
    Classifications
    LC ClassificationsQA278.75
    ID Numbers
    Open LibraryOL22253328M
    ISBN 10376433021X

    † Nonparametric procedures require fewer assumptions than the traditional meth-ods so that they can be used more widely than the corresponding parametric methods. In particular, nonparametric procedures are applicable and more ef-flcient in many situations where normality assumption is violated. 1. Contact D. H. Hill Jr. Library. 2 Broughton Drive Campus Box Raleigh, NC () James B. Hunt Jr. Library. Partners WayCited by: 3.

    Book Description. A Practical Guide to Implementing Nonparametric and Rank-Based Procedures. Nonparametric Statistical Methods Using R covers traditional nonparametric methods and rank-based analyses, including estimation and inference for models ranging from simple location models to general linear and nonlinear models for uncorrelated and correlated responses. Book series editor: Chapman & Hall/CRC Biostatistics series {. Chair: Biometrics Section, American Statistical Association, R.E. and Turnbull, B.W. (). \Two (k+l)-decision selection procedures for comparing k normal means with a flxed known standard," Journal of the American Statistical Association, cedures for sequential File Size: KB.

    general class of parametric and nonparametric model selection problems. To select a model, we can feed our single-step p-values as inputs into sequential stopping rules such as those proposed by G’Sell et al. () and Li and Barber (), achieving controlFile Size: 1MB. mentions some additional important nonparametric problems. Finally, Section 4 contains key references to the nonparametric literature.!_/ 1. One-Sample Problems. In the following, a sample (7 observations) will be used to illustrate how, when, and with what consequences nonparametric procedures can be used.


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Nonparametric sequential selection procedures by H. BuМ€ringer Download PDF EPUB FB2

Nonparametric Sequential Selection Procedures. Authors: BÜRINGER, MARTIN, SCHRIEVER. Free Preview. Buy this book eB40 € price for Spain (gross) Buy eBook ISBN ; Digitally watermarked, DRM-free; Included format: PDF; ebooks can be used on all reading devices.

: Nonparametric Sequential Selection Procedures (): BÜRINGER, MARTIN, SCHRIEVER: BooksCited by: Additional Physical Format: Online version: Büringer, H., Nonparametric sequential selection procedures. Boston: Birkhauser, © (OCoLC) Sequential Procedures for Selecting the Best of k ≥ 2 Binomial Populations.

Front Matter. Pages N1-N1. Get this from a library. Nonparametric sequential selection procedures. [H Büringer; H Martin; K -H Schriever] -- New (a£.) !pea.d abJtoad only -t6 they Me -tVl aVl tve -tVled pJte-6evttatioVl.

It WM ~ l that made M. This chapter discusses some parametric and nonparametric sequential subset selection procedures. In sequential selection and ranking procedures, the best population is selected by using the indifference zone approach.

Multiple decision procedures can be defined that select a subset of a Cited by: 5. nonparametric sequential selection procedures genera 1 i ntroduc ti on part 1 sequential procedures for selecting the best of k~2 binomial populations introducti on chapter 1 selection procedures with unrestricted patient horizon.

The book can be used as a primary text or supplement in a course on applied nonparametric or robust procedures and as a reference for researchers who need to implement nonparametric and rank-based methods in practice.

Through numerous examples, it shows readers how to apply these methods using by: for the nonparametric regression estimators. Since sequential procedures allow an experimenter to make decisions based on the smallest number of observations without compromising accuracy, application of sequential pro-cedures to a nonparametric regression model.

This chapter discusses some parametric and nonparametric sequential subset selection procedures. In sequential selection and ranking procedures, the best population is selected by using the indifference zone approach.

Multiple decision procedures can be defined that select a subset of a. Handbook of Parametric and Nonparametric Statistical Procedures single-sample runs test 3.

Extension of the runs test to data with more than two categories 4. Test 10a: The runs test for serial randomness VII. Additional Discussion of the Single-Sample Runs Test 1. Additional discussion of the concept of randomness Size: 1MB.

Nonparametric tests, on the other hand, do not require any strict distributional assumptions. Even if the data are distributed normally, nonparametric methods are often almost as powerful as parametric methods.

Many nonparametric methods analyze the ranks of. Nonparametric sequential confldence sets 5. Nonparametrics in change-point modeis References Sequential Ranking and Selection Procedures Shanti S.

Gupta and S. Panchapakesan 1. Introduction 2. Some general aspects of sequential selection procedures 3.

Selection from normal populations: IZ approach 4. $\begingroup$ Nonparametric statistics is a large field, but I'd guess All of Nonparametric Statistics by Larry Wassermann should be a reasonable starting point. I don't know if I would call it "advanced level" but he sketches the proofs of many of the results in the book.

A Sequential Nonparametric Two-Sample Test. Unlike other adaptive model selection procedures such as the Akaike information criterion AIC and leave-one-out cross-validation, BIC and Bayes. Praise for the Second Edition “This book should be an essential part of the personal library of every practicing statistician.”—Technometrics Thoroughly revised and updated, the new edition of Nonparametric Statistical Methods includes additional modern topics and procedures, more practical data sets, and new problems from real-life situations.

Two procedures are proved to be asymptotically optimal for different settings. First we study the problem of bandwidth selection for nonparametric pointwise kernel estimation with a given kernel. Parametric and Nonparametric Sequential Change Detection in R: The cpm Package Gordon J.

Ross University College London Abstract The change point model framework introduced inHawkins, Qiu, and Kang() and Hawkins and Zamba(a) provides an e ective and computationally e cient methodCited by: Fundamentals of Nonparametric Bayesian Inference is the first book to comprehensively cover models, methods, and theories of Bayesian nonparametrics.

Readers can learn basic ideas and intuitions as well as rigorous treatments of underlying theories and computations from this wonderful book.'Cited by: Robust Nonparametric Statistical Methods - CRC Press Book Presenting an extensive set of tools and methods for data analysis, Robust Nonparametric Statistical Methods, Second Edition covers univariate tests and estimates with extensions to linear models, multivariate models, times series models, experimental designs, and mixed models.

Nonparametric methods are growing in popularity and influence for a number of reasons. The main reason is that we are not constrained as much as when we use a parametric method. We do not need to make as many assumptions about the population that we are working with as what we have to make with a parametric method.

Nonparametric Statistical Methods, Third Edition is an excellent reference for applied statisticians and practitioners who seek a review of nonparametric methods and their relevant applications. The book is also an ideal textbook for upper-undergraduate and first-year graduate courses in applied nonparametric statistics.4/5(14).The first nonparametric regression estimate of local averaging there is a growing demand for procedures leaving the researcher with no other choice but a nonparametric approach.

This book presents a modern approach to nonparametric regression with random design. The starting point is a prediction problem where mini.