Por Andrey N. (Intel), adicionado

Intel® Summary Statistics Library is now available as part of the Vector Statistical Library in the Intel® Math Kernel Library 10.3 Beta download. Therefore it has been removed as a download from this page. We would like to thank you for participating in the evaluation of the Intel® Summary Statistics Library, and also for the feedback that helped us improve its quality. What If Home | Product Overview | Intel® TM ABI specification | Technical Requirements FAQ | Primary Technology Contacts | Discussion Forum | Blog |

### Intel® Summary Statistics Library 1.0 Update

## Product Overview

Intel® Summary Statistics Library is a set of algorithms for parallel processing of multi-dimensional datasets. It contains functions for initial analysis of raw data which allow investigating structure of datasets and get their basic characteristics, estimates, and internal dependencies.

## Features and Benefits

The library provides rich set of tools for estimation of various statistical characteristics of a dataset:

**Basic statistics.** Algebraic and central moments up to 4th order, skewness, kurtosis, variation coefficient, quantiles and order statistics.

**Estimation of Dependencies. **Variance-covariance/correlation matrix, partial variance-covariance/correlation matrix, pooled/group variance-covariance/correlation matrix.

**Data with Outliers.** The Intel® Summary Statistics Library contains a tool for detection of outliers in a dataset. Also the library allows computing robust estimates of the covariance matrix and mean in presence of outliers.

**Missing Values. **Data which contains missing values can be effectively processed using modern algorithms implemented in the package.

**Out-of-Memory Datasets.** Many algorithms of the library support data which cannot fit into the physical memory processing huge data arrays in portions. Specifically, variance-covariance matrix estimators, algebraic and central moments, skewness, kurtosis, and variation coefficient can process a dataset in portions.

**Various Data Storage Formats.** The Intel® Summary Statistics Library supports in-rows and in-columns storage formats for datasets, full and packed format for variance-covariance matrix.

The Intel® Summary Statistics Library uses recent advances of statistics by providing modern algorithms that enhance accuracy and performance of statistical computations. The library is optimized for latest multi-core Intel processors what allows to achieve significant performance benefits compared to traditional statistical packages and libraries.

## Feedback

We are constantly striving to give you the most effective development tools for your business. To help us shape our tools to your needs, please take a minute to answer 12 short questions. Your feedback is critical to the success of our product and will help drive the future direction of the Intel® Math Kernel Library.

## Intel® Summary Statistics Library Blogs

- Intel® Summary Statistics Library: how to manage with oceans of information?
- Intel® Summary Statistics Library: several estimates at one stroke.
- Intel® Summary Statistics Library: how to process data in chunks?
- Intel® Summary Statistics Library: how to detect outliers in datastes?
- Intel® Summary Statistics Library: why not to use multi-core advantages?
- Intel® Summary Statistics Library: what is new in the Update?
- Intel® Summary Statistics Library: how fast is the algorithm for detection of outliers?
- Intel® Summary Statistics Library: how to use the robust methods?
- Intel® Summary Statistics Library: how to deal with missing observations?
- Intel® Summary Statistics Library: hot to compute quantiles for streaming data?

## Technical Requirements

1. To use Intel® Summary Statistics Library you must have a license for Intel® MKL product on your system. If you don’t, you can acquire the commercial product or try an evaluation copy.

2. Please see Install Guide for more details on technical requirements, including the list of supported processors and operating systems.

## Frequently Asked Questions

**Q - What programming interfaces does Intel® Summary Statistics Library support?**

A - C and Fortran90/95

**Q - Do I need additional software to use Intel® Summary Statistics Library?**

A - Intel® MKL library is necessary to use Intel® Summary Statistics Library

**Q - What do I need to get started for developing application with the Intel® Summary Statistics Library?**

A - Intel® Summary Statistics Library is now available in Intel® Math Kernel Library 10.3 Beta. Therefore it has been removed as a download from this page .

**Q - Where can I get support for the Intel® Summary Statistics Library?**

A - You are welcome to join our Intel® Summary Statistics Library forum and post your questions and feedback.

## Developer Support Team

**Andrey Nikolaev**

Andrey Nikolaev is a Senior Software Engineer at Software & Solutions Group. He holds primary degree in Applied Mathematics from Lomonosov Moscow State University, branch in city of Ulyanovsk and Ph.D. degree in Mathematical Cybernetics from Ulyanovsk State University. His work is related to design, development and optimization of statistical algorithms, optimization of financial algorithms, modeling and data analysis. Prior joining Intel he was involved in design of real-time SW for communication system in scientific industrial company.

**Ilya Burylov**

Ilya Burylov is a Senior Software Engineer at Software & Solutions Group. He holds a Master’s degree in Applied Mathematics from Perm State Technical University. Since joining Intel Ilya works on various problems related to scientific computation, design and development of sequential and parallel numerical algorithms, optimization of computational algorithms of Financial Mathematics.

**Dmitry Kabaev**

Dmitry Kabaev is a Senior Software Engineer at Software & Solutions Group. He holds a radio-physicist’s degree from Nizhniy Novgorod State University and a Ph.D. degree in Telecommunication from Federal Unitary Enterprise ‘Polyot’ (Russia). Since joining Intel his work is related to statistical data analysis, development and optimization of statistical numerical algorithms. -->

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