Application Notes for Intel® oneAPI Math Kernel Library Summary Statistics

Download PDF
ID 772991
Date 12/04/2020
Public

A newer version of this document is available. Customers should click here to go to the newest version.

Document Table of Contents
Document Table of Contents x
Application Notes for Intel® oneAPI Math Kernel Library Summary Statistics
Application Notes for Intel® oneAPI Math Kernel Library Summary Statistics x
Algorithms and Interfaces in Summary Statistics Common Usage Model of Summary Statistics Dealing With Out-Of-Memory Datasets Working with Data that Has Outliers and Missing Values Legal Information 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
About This Document x
Conventions and Symbols
Algorithms and Interfaces in Summary Statistics x
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
Estimating Raw and Central Moments and Sums, Skewness, Excess Kurtosis, Variation, and Variance-Covariance/Correlation/Cross-Product Matrix x
Computing Estimations for Large Datasets Calculating Multiple Estimates
Estimating Quantiles x
Computing Quantiles for Streaming Data with VSL_SS_METHOD_SQUANTS_ZW
Handling Missing Values in Matrices of Observations x
Basic Assumptions for the MI Method Basic Components of the MI Method
Detecting Outliers in Datasets x
Using the BACON Algorithm for Outlier Detection Using Robust Methods
Application Notes for Intel® oneAPI Math Kernel Library Summary Statistics

Application Notes for Intel® oneAPI Math Kernel Library Summary Statistics

Summary Statistics is a subcomponent of the Vector Statistics domain of Intel® oneAPI Math Kernel Library. Summary Statistics provides you with functions for initial statistical analysis, and offers solutions for parallel processing of multi-dimensional datasets.

These Application Notes cover the algorithms, interfaces, usage models, and the most important features of Summary Statistics.

Algorithms and Interfaces in Summary Statistics

See Algorithms and Interfaces in Summary Statistics for information on methods and usage specifics of Summary Statistics algorithms.

Common Usage Model of Summary Statistics

Common Usage Model of Summary Statistics gives you an idea of what a typical Summary Statistics application looks like.

Dealing With Out-Of-Memory Datasets

If you have to deal with large datasets that do not fit into your target system’s memory, consider Processing Data in Blocks. The Computing Quantiles for Streaming Data section covers some steps that are specific to estimating quantiles for out-of-memory data.

Working with Data that Has Outliers and Missing Values

The chapters Detecting Outliers in Datasets and Dealing with Missing Observations can help you process imperfect real-world data.

Level Two Title