Application Notes for Intel® oneAPI Math Kernel Library Summary Statistics
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Algorithms and Interfaces in Summary Statistics
This section discusses different methods and usage specifics of the Summary Statistics algorithms. For some methods, interfaces are described. For details on the Summary Statistics API, see [MKLMan].
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- 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 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
- 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