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Source: Robust measures of scale

In statistics, robust measures of scale are methods that quantify the statistical dispersion in a sample of numerical data while resisting outliers. The most common such robust statistics are the interquartile range (IQR) and the median absolute deviation (MAD). These are contrasted with conventional or non-robust measures of scale, such as sample standard deviation, which are greatly influenced by outliers.
These robust statistics are particularly used as estimators of a scale parameter, and have the advantages of both robustness and superior efficiency on contaminated data, at the cost of inferior efficiency on clean data from distributions such as the normal distribution. To illustrate robustness, the standard deviation can be made arbitrarily large by increasing exactly one observation (it has a breakdown point of 0, as it can be contaminated by a single point), a defect that is not shared by robust statistics.

IQR and MAD

robust

Estimation

Efficiency

robust

measures

Absolute pairwise differences

where

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These can be computed in O(n log n) time and O(n) space.
Neither of these requires location estimation, as they are based only on differences between values. They are both more efficient than the MAD under a Gaussian distribution: Sn is 58% efficient, while Qn is 82% efficient.
For a sample from a normal distribution, Sn is approximately unbiased for the population standard deviation even down to very modest sample sizes (<1% bias for n = 10).
For a large sample from a normal distribution, 2.22Qn is approximately unbiased for the population standard deviation. For small or moderate samples, the expected value of Qn under a normal distribution depends markedly on the sample size, so finite-sample correction factors (obtained from a table or from simulations) are used to calibrate the scale of Qn.

The biweight midvariance

Like Sn and Qn, the biweight midvariance aims to be robust without sacrificing too much efficiency. It is defined as

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{\displaystyle {\frac {n\sum _{i=1}^{n}(x_{i}-Q)^{2}(1-u_{i}^{2})^{4}I(|u_{i}|<1)}{\left(\sum _{i}(1-u_{i}^{2})(1-5u_{i}^{2})I(|u_{i}|<1)\right)^{2}}},}

where I is the indicator function, Q is the sample median of the Xi, and

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{\displaystyle u_{i}={\frac {x_{i}-Q}{9\cdot {\rm {MAD}}}}.}

Its square root is a robust estimator of scale, since data points are downweighted as their distance from the median increases, with points more than 9 MAD units from the median having no influence at all.

Extensions

Mizera & Müller (2004) propose a robust depth-based estimator for location and scale simultaneously. They propose a new measure named the Student median.

Confidence intervals

A robust confidence interval is a robust modification of confidence intervals, meaning that one modifies the non-robust calculations of the confidence interval so that they are not badly affected by outlying or aberrant observations in a data-set.

= Example

=
In the process of weighing 1000 objects, under practical conditions, it is easy to believe that the operator might make a mistake in procedure and so report an incorrect mass (thereby making one type of systematic error). Suppose there were 100 objects and the operator weighed them all, one at a time, and repeated the whole process ten times. Then the operator can calculate a sample standard deviation for each object, and look for outliers. Any object with an unusually large standard deviation probably has an outlier in its data. These can be removed by various non-parametric techniques. If the operator repeated the process only three times, simply taking the median of the three measurements and using σ would give a confidence interval. The 200 extra weighings served only to detect and correct for operator error and did nothing to improve the confidence interval. With more repetitions, one could use a truncated mean, discarding the largest and smallest values and averaging the rest. A bootstrap calculation could be used to determine a confidence interval narrower than that calculated from σ, and so obtain some benefit from a large amount of extra work.
These procedures are robust against procedural errors which are not modeled by the assumption that the balance has a fixed known standard deviation σ. In practical applications where the occasional operator error can occur, or the balance can malfunction, the assumptions behind simple statistical calculations cannot be taken for granted. Before trusting the results of 100 objects weighed just three times each to have confidence intervals calculated from σ, it is necessary to test for and remove a reasonable number of outliers (testing the assumption that the operator is careful and correcting for the fact that he is not perfect), and to test the assumption that the data really have a normal distribution with standard deviation σ.

Computer simulation

The theoretical analysis of such an experiment is complicated, but it is easy to set up a spreadsheet which draws random numbers from a normal distribution with standard deviation σ to simulate the situation; this can be done in Microsoft Excel using =NORMINV(RAND(),0,σ)), as discussed in and the same techniques can be used in other spreadsheet programs such as in OpenOffice.org Calc and gnumeric.
After removing obvious outliers, one could subtract the median from the other two values for each object, and examine the distribution of the 200 resulting numbers. It should be normal with mean near zero and standard deviation a little larger than σ. A simple Monte Carlo spreadsheet calculation would reveal typical values for the standard deviation (around 105 to 115% of σ). Or, one could subtract the mean of each triplet from the values, and examine the distribution of 300 values. The mean is identically zero, but the standard deviation should be somewhat smaller (around 75 to 85% of σ).