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About This Document
About Summary Statistics
Algorithms and Interfaces in Summary Statistics
Common Usage Model of Summary Statistics Algorithms
Processing Data in Blocks
Detecting Outliers in Datasets
Dealing with Missing Observations
Computing Quantiles for Streaming Data
Bibliography

Estimating Raw and Central Moments and Sums, Skewness, Excess Kurtosis, Variation, and Variance-Covariance/Correlation/Cross-Product Matrix
Computing Median Absolute Deviation
Computing Mean Absolute Deviation
Computing Minimum/Maximum Values
Calculating Order Statistics
Estimating Quantiles
Estimating a Pooled/Group Variance-Covariance Matrices/Means
Estimating a Partial Variance-Covariance Matrix
Performing Robust Estimation of a Variance-Covariance Matrix
Detecting Multivariate Outliers
Handling Missing Values in Matrices of Observations
Parameterizing a Correlation Matrix
Sorting an Observation Matrix

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## Calculating Order Statistics

Order statistics is stored as a one-dimensional array. To hold results of the calculations, the size of this array should be at least `m`*`n`

where

`m`is the number of vector components to process.`n`is the number of observations.

The calculation results are packed according to the value of the `ostatsstorage` variable`.` For the supported storage formats, please see table Storage format of matrix of observations and order statistics in the Summary Statistics section of [MKLMan].

**Parent topic:**Algorithms and Interfaces in Summary Statistics