A Mixture-Model Approach for Parallel Testing for Unequal Variances
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Haim Y. Bar
Testing for unequal variances is usually performed in order to check the validity of the assumptions that underlie standard tests for differences between means (the t-test and anova). However, existing methods for testing for unequal variances (Levene's test and Bartlett's test) are notoriously non-robust to normality assumptions, especially for small sample sizes. Moreover, although these methods were designed to deal with one hypothesis at a time, modern applications (such as to microarrays and fMRI experiments) often involve parallel testing over a large number of levels (genes or voxels). Moreover, in these settings a shift in variance may be biologically relevant, perhaps even more so than a change in the mean. This paper proposes a parsimonious model for parallel testing of the equal variance hypothesis. It is designed to work well when the number of tests is large; typically much larger than the sample sizes. The tests are implemented using an empirical Bayes estimation procedure which `borrows information' across levels. The method is shown to be quite robust to deviations from normality, and to substantially increase the power to detect differences in variance over the more traditional approaches even when the normality assumption is valid.
©2012 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
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- Stopping-Time Resampling and Population Genetic Inference under Coalescent Models
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Articles in the same Issue
- Article
- The Inheritance Procedure: Multiple Testing of Tree-structured Hypotheses
- Optimality Criteria for the Design of 2-Color Microarray Studies
- Stopping-Time Resampling and Population Genetic Inference under Coalescent Models
- A Mixture-Model Approach for Parallel Testing for Unequal Variances
- Fast Identification of Biological Pathways Associated with a Quantitative Trait Using Group Lasso with Overlaps
- MicroRNA Transcription Start Site Prediction with Multi-objective Feature Selection
- A Context Dependent Pair Hidden Markov Model for Statistical Alignment
- Fast Wavelet Based Functional Models for Transcriptome Analysis with Tiling Arrays
- Alignment-free Sequence Comparison for Biologically Realistic Sequences of Moderate Length
- Transcriptional Network Inference from Functional Similarity and Expression Data: A Global Supervised Approach
- Improving Hidden Markov Models for Classification of Human Immunodeficiency Virus-1 Subtypes through Linear Classifier Learning
