======================================================
Variance Estimation with Mean Absolute Deviation (MAD)
======================================================
:doc:`/SingleStepClassifiers/NonCausalStep/index`

This step applies the Mean Absolute Deviation (MAD) algorithm to estimate the variance of the data. The basic formula is as follows:



.. math::

   {\hat{\sigma} = K \cdot \text{MAD}}\quad\text{,}

where :math:`K \approx 1.4826`\ , MAD is a measure returned by the Mean Absolute Deviation (MAD) algorithm and :math:`\hat{\sigma}` denotes the estimated variance.

.. rubric:: Input Parameters

1. Input data

.. rubric:: Output Parameters

1. Estimated variance

.. rubric:: Workflow

.. image:: workflow.svg


.. rubric:: Algorithm

:doc:`/Algorithms/MeanAbsoluteDeviation/index`

.. rubric:: References

- D.C.\  Hoaglin, F. Mosteller, J. W. Tukey, Understanding Robust and Exploratory Data Analysis, John Wiley and Sons, 1983.