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Related Concept Videos

Regression Toward the Mean01:52

Regression Toward the Mean

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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
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Linearization and Approximation01:26

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Linearization is a mathematical technique used to approximate complex, nonlinear functions with simpler linear models in the vicinity of a chosen reference point. The method is based on the idea that, although a function may be difficult to evaluate exactly, its behavior near a specific input value can often be closely approximated by the tangent line at that point. This approach is particularly useful when small deviations from a known value are involved.Consider the square root function, for...
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Mean Absolute Deviation01:13

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The mean absolute deviation is also a measure of the variability of data in a sample. It is the absolute value of the average difference between the data values and the mean.
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Standard Error of the Mean01:13

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The sampling variability of a statistic is defined as how much the statistic varies from one sample to another. The sampling variability of a statistic is typically measured by measuring its standard error.
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A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
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Related Experiment Videos

R package MVR for Joint Adaptive Mean-Variance Regularization and Variance Stabilization.

Jean-Eudes Dazard1, Hua Xu1, J Sunil Rao2

  • 1Division of Bioinformatics, Center for Proteomics and Bioinformatics, Case Western Reserve University. Cleveland, OH 44106, USA.

Proceedings. American Statistical Association. Annual Meeting
|January 29, 2016
PubMed
Summary

This study introduces the R package MVR for non-parametric joint adaptive mean-variance regularization and variance stabilization. It addresses challenges in high-dimensional

Keywords:
High-Dimensional DataMean-Variance EstimationParallel ProgrammingR packageRegularization and Variance StabilizationRegularized Test-statistics

Related Experiment Videos

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Statistical Computing

Background:

  • High-dimensional 'omics' data present challenges like mean-variance relationships and low statistical power.
  • Traditional methods struggle with unreliable variance estimators and limited degrees of freedom in such datasets.

Purpose of the Study:

  • To present an R implementation of a non-parametric joint adaptive mean-variance regularization and variance stabilization procedure.
  • To provide a user-friendly tool for analyzing high-dimensional multivariate datasets, particularly in 'omics' research.

Main Methods:

  • Developed a non-parametric procedure for joint adaptive mean-variance regularization and variance stabilization.
  • Implemented the procedure in the R language, utilizing C interfacing for computational efficiency.
  • Included features for normalization, variance stabilization, computation of regularized statistics, and diagnostic plots.

Main Results:

  • The MVR package offers a comprehensive solution for mean-variance regularization and variance stabilization.
  • The implementation is computationally efficient, supports parallel computing, and includes synthetic and real 'omics' datasets for testing.
  • Features are designed for user-friendliness, requiring minimal user interaction per functionality.

Conclusions:

  • The MVR R package provides a robust and efficient tool for analyzing complex high-dimensional 'omics' data.
  • It effectively handles challenges related to mean-variance dependency and low degrees of freedom.
  • The package is readily available from CRAN, promoting its adoption in the scientific community.