# Difference between revisions of "Madc"

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(Created page with "===Description=== The '''madc''' function is a scale estimator given by the Median Absolute Deviation (with finite sample correction factor). It is defined as: mad(x)= b_n 1...") |
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The '''madc''' function is a scale estimator given by the Median Absolute Deviation (with finite sample correction factor). | The '''madc''' function is a scale estimator given by the Median Absolute Deviation (with finite sample correction factor). | ||

It is defined as: | It is defined as: | ||

− | + | madc(x)= b_n 1.4826 med(|x_i - med(x)|) | |

with <code>b_n</code> a small sample correction factor to make the mad unbiased at the normal distribution. It can resist 50% outliers. If <code>x</code> is a matrix, the scale estimate is computed on the columns of <code>x</code>. The result is then a row vector. If <code>x</code> is a row or a column vector, the output is a scalar. | with <code>b_n</code> a small sample correction factor to make the mad unbiased at the normal distribution. It can resist 50% outliers. If <code>x</code> is a matrix, the scale estimate is computed on the columns of <code>x</code>. The result is then a row vector. If <code>x</code> is a row or a column vector, the output is a scalar. | ||

## Latest revision as of 10:48, 5 December 2019

## Contents

### Description

The **madc** function is a scale estimator given by the Median Absolute Deviation (with finite sample correction factor).
It is defined as:

madc(x)= b_n 1.4826 med(|x_i - med(x)|)

with `b_n`

a small sample correction factor to make the mad unbiased at the normal distribution. It can resist 50% outliers. If `x`

is a matrix, the scale estimate is computed on the columns of `x`

. The result is then a row vector. If `x`

is a row or a column vector, the output is a scalar.

This function is part of LIBRA: the Matlab Library for Robust Analysis, available at:

http://wis.kuleuven.be/stat/robust.html

Written by S.Verboven

### Synopsis

result = madc(x);

#### Inputs

**x**either a data matrix with n observations in rows, p variables in columns or a column vector of length n.