Resampling-Based Multiple Testing: Examples and Methods for p-Value Adjustment
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.
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Munchausens Statistical Grid
Using Software for the Analysis
List of Algorithms
adjusted p-values Algorithm allows alternative analysis applications approximately associated assumed binary binomial Bonferroni bootstrap Chapter collection combined comparing comparisons complete null computed condition considered continuous contrasts correlations COUNT data sets defined denote dependence discussed distribution Dose effects equal error estimate exact example experiment experimental extremely Figure function given gives groups important incorporating increase independent indicated interest lead least less linear matrix mean measures multiple testing multiplicity adjustment multivariate normal Note null hypothesis observed original particular performed permutation pivotality pooled possible probability problem PROC MULTTEST procedure provides questions random regression rejected replacement reported resampling method resampling-based residuals response sample sizes scores shows significant simulation specific standard step-down structure suggest Table test statistics treatment treatment groups trend true tumor Type unadjusted usually values variables variance vectors weight zero
Randomization, Bootstrap and Monte Carlo Methods in Biology, Third Edition
Bryan F.J. Manly
Обмежений попередній перегляд - 2006
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