9 edition of Resampling-based multiple testing found in the catalog.
|Statement||Peter H. Westfall, S. Stanley Young.|
|Series||Wiley series in probability and mathematical statistics.|
|Contributions||Young, S. Stanley, 1943-|
|LC Classifications||QA278.8 .W47 1993|
|The Physical Object|
|Pagination||xvii, 340 p. :|
|Number of Pages||340|
|LC Control Number||92022194|
Book Description. Useful Statistical Approaches for Addressing Multiplicity Issues Includes practical examples from recent trials. Bringing together leading statisticians, scientists, and clinicians from the pharmaceutical industry, academia, and regulatory agencies, Multiple Testing Problems in Pharmaceutical Statistics explores the rapidly growing area of multiple comparison research with an. The new procedure can be used easily for estimating the p-value for any resampling-based test. We show through numeric simulations that the proposed procedure can be – times as efficient (in term of computing time) as the standard resampling-based procedure when evaluating a test statistic with a small p-value (e.g. less than 10 Cited by:
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): The burgeoning field of genomics has revived interest in multiple testing procedures by raising new methodological and computational challenges. For example, microarray experiments generate large multiplicity problems in which thousands of hypotheses are tested simultaneously. Multiple testing. In this simulation study, the four robust resampling-based testing methods to be compared are formed by combining the determinant-based statistic and the eigenvalue-based statistic with b = and b = The H-D bootstrap is not considered for multiple by: 1.
Choice of null distribution in resampling based multiple testing Article in Journal of Statistical Planning and Inference () October with 55 Reads How we measure 'reads'. A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text.
Case studies in business administration
In the Cage and Other Stories (Penguin Modern Classics)
IT and the national curriculum.
art of pantomime.
Our child--Gods child.
ecology and management of wood in world rivers
The trivmphs of integrity
Kentucky settlement and statehood, 1750-1800
Basic Trigonometry (Milwaukee Area Technical College Mathematics Series)
The language of life
Motor goods distributors.
Municipal Administration in the Roman Empire
Peter H. Westfall and S. Stanley Young are the authors of Resampling-Based Multiple Testing: Examples and Methods for p-Value Adjustment, published by by: Combines recent developments in resampling technology (including the bootstrap) with new methods for multiple testing that are easy to use, convenient to report and widely applicable.
Software from SAS Institute is available to execute many of the methods and programming is straightforward for Price: $ Peter H. Westfall and S. Stanley Young are the authors of Resampling-Based Multiple Testing: Examples and Methods for p-Value Adjustment, published by Wiley.
Peter H. Westfall and S. Stanley Young are the authors of Resampling-Based Multiple Testing: Examples and Methods for p-Value Adjustment, published by Wiley. show more Learn about new. test statistics. In their book, Westfall & Young propose resampling-based p-value adjustment pro-cedures which are highly relevant to microarray experiments.
In particular, these authors de ne adjusted p-values for multiple testing procedures which control the family-wise error. In fatc Peter Westfall being a coauthor of this book undoubtedly wrote section on "Resampling-Based Multiple Comparison Procedures." Nice stand-out features of the book are its clarity and conciseness, coverage of group sequential and adaptive designs and the nice illustration through actual problems using R software and the CRAN library of R routines for multiple s: 2.
Resampling-Based Multiple Testing for Microarray Data Analysis by Yongchao Ge, Sandrine Dudoit, Terence P. Speed, The burgeoning field of genomics has revived interest in multiple testing procedures by raising new methodological and computational challenges.
Bioconductor resampling-based multiple hypothesis testing with Applications to Genomics. permtest: an R package to compare the variability within and distance between two groups within a set of microarray data.
Bootstrap Resampling: interactive demonstration of hypothesis testing. Specifically, the book proposes resampling-based single-step and stepwise multiple testing procedures for controlling a broad class of Type I error rates, defined as tail probabilities and expected values for arbitrary functions of the numbers of Type I errors and rejected hypotheses (e.g.
It also covers modern approaches for adjusting p-values such as bootstrap and permutation resampling as given in Westfall and Young's Resampling Based Multiple Testing (). In fatc Peter Westfall being a coauthor of this book undoubtedly wrote section on "Resampling-Based Multiple Reviews: 2. (). Resampling-Based Multiple Testing: Examples and Methods for p-Value Adjustment.
Technometrics: Vol. 35, No. 4, pp. Cited by: “Resampling” is a general term that encompasses bootstrap, permutation, and parametric simulation-based analyses; “resampling-based multiple testing” refers to the use of such methods in multiple testing by: A self-contained introduction to multiple comparison procedures, this book offers strategies for constructing the procedures and illustrates the framework for multiple hypotheses testing in general parametric models.
It is suitable for readers with R experience but limited knowledge of multiple comparison procedures and vice versa. Resampling-based multiple testing: examples and methods for P-value adjustment.
Statistical Resampling -- Multiple Testing Controversies -- A Structure for Multiple Testing Data Analysis -- 2. Resampling-Based Adjustments: Basic Concepts -- Definitions and Preliminary Material -- # Resampling-based multiple testing.
Buy Resampling-Based Multiple Testing: Examples and Methods for P-value Adjustment (Wiley Series in Probability and Statistics) by Westfall, Young (ISBN: ) from Amazon's Book Store.
Everyday low prices and free delivery on eligible : Westfall, Young. Stepdown multiple testing procedures are generally based on a set of null resampling test statistics t ∗, m ≔ (t 1 ∗, m,t S ∗, m), for m = 1,M, where M denotes the number of resampling repetitions.
Depending on context, the resampling can be carried out by a bootstrap method, a permutation method, or a randomization by: Combines recent developments in resampling technology (including the bootstrap) with new methods for multiple testing that are easy to use, convenient to report and widely applicable.
Software from SAS Institute is available to execute many of the methods and programming is straightforward for other applications. Explains how to summarize results using adjusted p-values which do not.
Typical testing scenarios are illustrated by applying various MTPs implemented in multtest to the Acute Lymphoblastic Leukemia (ALL) data set of Chiaretti et al.
(), with the aim of identifying genes whose expression measures are associated with (possibly censored) biological and clinical outcomes. Resampling-based methods for multiple hypothesis testing often lead to long run times when the number of tests is large.
This paper presents a simple rule that substantially reduces computation by allowing resampling to terminate early on a subset of by: Resampling-based multiple testing for microarray data hypothesis (with Discussion) Article in Test 12(1) February with 38 Reads How we measure 'reads'.
Get this from a library! Resampling-based multiple testing: examples and methods for p-value adjustment. [Peter H Westfall; S Stanley Young].Resampling-Based Hypothesis Tests∗, Journal of Clinical Child & Adolescent Psychology, DOI: / To link to this article: g/The biological question of differential expression can be restated as a problem in multiple hypothesis testing: the simultaneous test for each gene of the null hypothesis of no association between the expression levels and the responses or covariates.