Resampling Based Multiple Testing
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| Author | : Peter H. Westfall |
| Publisher | : John Wiley & Sons |
| Total Pages | : 382 |
| Release | : 1993-01-12 |
| Genre | : Mathematics |
| ISBN | : 9780471557616 |
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 necessitate cumbersome table look-ups. Demonstrates how to incorporate logical constraints among hypotheses, further improving power.
| Author | : Sandrine Dudoit |
| Publisher | : Springer |
| Total Pages | : 0 |
| Release | : 2010-11-25 |
| Genre | : Science |
| ISBN | : 9781441923790 |
This book establishes the theoretical foundations of a general methodology for multiple hypothesis testing and discusses its software implementation in R and SAS. These are applied to a range of problems in biomedical and genomic research, including identification of differentially expressed and co-expressed genes in high-throughput gene expression experiments; tests of association between gene expression measures and biological annotation metadata; sequence analysis; and genetic mapping of complex traits using single nucleotide polymorphisms. The procedures are based on a test statistics joint null distribution and provide Type I error control in testing problems involving general data generating distributions, null hypotheses, and test statistics.
| Author | : Frank Bretz |
| Publisher | : CRC Press |
| Total Pages | : 202 |
| Release | : 2016-04-19 |
| Genre | : Mathematics |
| ISBN | : 1420010905 |
Adopting a unifying theme based on maximum statistics, Multiple Comparisons Using R describes the common underlying theory of multiple comparison procedures through numerous examples. It also presents a detailed description of available software implementations in R. The R packages and source code for the analyses are available at http://CRAN.R-project.org After giving examples of multiplicity problems, the book covers general concepts and basic multiple comparisons procedures, including the Bonferroni method and Simes’ test. It then shows how to perform parametric multiple comparisons in standard linear models and general parametric models. It also introduces the multcomp package in R, which offers a convenient interface to perform multiple comparisons in a general context. Following this theoretical framework, the book explores applications involving the Dunnett test, Tukey’s all pairwise comparisons, and general multiple contrast tests for standard regression models, mixed-effects models, and parametric survival models. The last chapter reviews other multiple comparison procedures, such as resampling-based procedures, methods for group sequential or adaptive designs, and the combination of multiple comparison procedures with modeling techniques. Controlling multiplicity in experiments ensures better decision making and safeguards against false claims. 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. See Dr. Bretz discuss the book.
| Author | : Alex Dmitrienko |
| Publisher | : CRC Press |
| Total Pages | : 323 |
| Release | : 2009-12-08 |
| Genre | : Mathematics |
| ISBN | : 1584889853 |
Useful Statistical Approaches for Addressing Multiplicity IssuesIncludes 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 c
| Author | : Michael Wolf |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2011 |
| Genre | : |
| ISBN | : |
It is common in econometric applications that several hypothesis tests are carried out at the same time. The problem then becomes how to decide which hypotheses to reject, accounting for the multitude of tests. In this paper, we suggest a stepwise multiple testing procedure which asymptotically controls the familywise error rate at a desired level. Compared to related single-step methods, our procedure is more powerful in the sense that it often will reject more false hypotheses. Unlike some stepwise methods, our method implicitly captures the joint dependence structure of the test statistics, which results in increased ability to detect alternative hypotheses. We prove our method asymptotically controls the familywise error rate under minimal assumptions. Some simulation studies show the improvements of our methods over previous proposals. We also provide an application to a set of real data.
| Author | : Phillip Good |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 238 |
| Release | : 2013-03-09 |
| Genre | : Mathematics |
| ISBN | : 1475723466 |
A step-by-step guide to the application of permutation tests in biology, medicine, science, and engineering. The intuitive and informal style makes this manual ideally suitable for students and researchers approaching these methods for the first time. In particular, it shows how to handle the problems of missing and censored data, nonresponders, after-the-fact covariates, and outliers.
| Author | : Robert Gentleman |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 478 |
| Release | : 2005-12-29 |
| Genre | : Computers |
| ISBN | : 0387293620 |
Full four-color book. Some of the editors created the Bioconductor project and Robert Gentleman is one of the two originators of R. All methods are illustrated with publicly available data, and a major section of the book is devoted to fully worked case studies. Code underlying all of the computations that are shown is made available on a companion website, and readers can reproduce every number, figure, and table on their own computers.
| Author | : Thomas M. Carsey |
| Publisher | : SAGE Publications |
| Total Pages | : 304 |
| Release | : 2013-08-05 |
| Genre | : Social Science |
| ISBN | : 1483324923 |
Taking the topics of a quantitative methodology course and illustrating them through Monte Carlo simulation, this book examines abstract principles, such as bias, efficiency, and measures of uncertainty in an intuitive, visual way. Instead of thinking in the abstract about what would happen to a particular estimator "in repeated samples," the book uses simulation to actually create those repeated samples and summarize the results. The book includes basic examples appropriate for readers learning the material for the first time, as well as more advanced examples that a researcher might use to evaluate an estimator he or she was using in an actual research project. The book also covers a wide range of topics related to Monte Carlo simulation, such as resampling methods, simulations of substantive theory, simulation of quantities of interest (QI) from model results, and cross-validation. Complete R code from all examples is provided so readers can replicate every analysis presented using R.
| Author | : Peter H. Westfall |
| Publisher | : SAS Press |
| Total Pages | : 0 |
| Release | : 2000 |
| Genre | : Computers |
| ISBN | : 9781580257596 |
Does your work require multiple inferences? Are you a statistics teacher looking for a study guide to supplement the usually incomplete or outdated multiple comparisons/multiple testing material in your textbook? This workbook, the companion guide written specifically for use with Multiple Comparisons and Multiple Tests Using the SAS System, provides the supplement you need. Use this workbook and you will find problems and solutions that will enhance your understanding of the material within the main text. The workbook also provides updated information about multiple comparisons procedures, including enhancements for Release 8.1 of the SAS System. The chapters correlate with the chapters of the main text, and the format is clear and easy to use. This book and the companion text are quite useful as supplements for learning multiple comparisons procedures in standard linear models, multivariate analysis, categorical analysis, and regression and nonparametric statistics. Book jacket.
| Author | : Peter C. Bruce |
| Publisher | : John Wiley & Sons |
| Total Pages | : 320 |
| Release | : 2015-01-08 |
| Genre | : Mathematics |
| ISBN | : 1118881338 |
Concise, thoroughly class-tested primer that features basic statistical concepts in the concepts in the context of analytics, resampling, and the bootstrap A uniquely developed presentation of key statistical topics, Introductory Statistics and Analytics: A Resampling Perspective provides an accessible approach to statistical analytics, resampling, and the bootstrap for readers with various levels of exposure to basic probability and statistics. Originally class-tested at one of the first online learning companies in the discipline, www.statistics.com, the book primarily focuses on applications of statistical concepts developed via resampling, with a background discussion of mathematical theory. This feature stresses statistical literacy and understanding, which demonstrates the fundamental basis for statistical inference and demystifies traditional formulas. The book begins with illustrations that have the essential statistical topics interwoven throughout before moving on to demonstrate the proper design of studies. Meeting all of the Guidelines for Assessment and Instruction in Statistics Education (GAISE) requirements for an introductory statistics course, Introductory Statistics and Analytics: A Resampling Perspective also includes: Over 300 “Try It Yourself” exercises and intermittent practice questions, which challenge readers at multiple levels to investigate and explore key statistical concepts Numerous interactive links designed to provide solutions to exercises and further information on crucial concepts Linkages that connect statistics to the rapidly growing field of data science Multiple discussions of various software systems, such as Microsoft Office Excel®, StatCrunch, and R, to develop and analyze data Areas of concern and/or contrasting points-of-view indicated through the use of “Caution” icons Introductory Statistics and Analytics: A Resampling Perspective is an excellent primary textbook for courses in preliminary statistics as well as a supplement for courses in upper-level statistics and related fields, such as biostatistics and econometrics. The book is also a general reference for readers interested in revisiting the value of statistics.
