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Abstract Details
Activity Number:
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335
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 2, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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Section for Statistical Programmers and Analysts
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Abstract - #302266 |
Title:
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R Package MVR for Joint Adaptive Mean-Variance Regularization and Variance Stabilization
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Author(s):
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Jean-Eudes J. Dazard*+ and Hua Xu and J. Sunil Rao
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Companies:
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Case Western Reserve University and Case Western Reserve University and University of Miami
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Address:
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10900 Euclid Avenue, Cleveland, OH, 44106,
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Keywords:
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R package ;
Parallel Programming ;
Mean-Variance Estimation ;
Regularization and Variance Stabilization ;
Regularized Test-statistics ;
High-Dimensional Data
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Abstract:
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We present an implementation in the R language for statistical computing of our recent non-parametric joint adaptive mean-variance regularization and variance stabilization procedure. The method is specifically suited for handling difficult problems posed by high-dimensional multivariate datasets, such as in 'omics'-type data, among which are that the variance is often a function of the mean, variable-specific estimators of variances are not reliable, and tests statistics have low powers due to a lack of degrees of freedom. The implementation offers a complete set of features including: (i) normalization and/or variance stabilization function, (ii) computation of mean-variance-regularized t- and F-statistics, (iii) generation of diverse diagnostic plots, (iv) synthetic and real 'omics' test datasets, (v) computationally efficient implementation, using C interfacing, and an option for parallel computing, (vi) manual and documentation on how to setup a cluster. To make each feature as user-friendly as possible, only one subroutine per functionality is to be handled by the end-user. It is available as an R package, called MVR (Mean-Variance Regularization), downloadable from the CRAN.
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