Multivariate Heavy Tail Phenomena: Modeling and Diagnostics

Abstract

This project develops reliable diagnostic, inferential and model validation tools for heavy tailed multivariate data; generates new classes of multivariate heavy tailed models that highlight the implications of dependence and tail weight; and applies these statistical and mathematical developments to the key application areas of network design and control, social network analysis, and cloud computing. Our application interests also include network security, anomaly detection, mobile application scheduling and risk analysis.

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Document Details

Document Type
Technical Report
Publication Date
Dec 26, 2018
Accession Number
AD1074082

Entities

People

  • Gennady Samorodnitsky
  • Lang Tong
  • Sidney Resnick

Organizations

  • Cornell University

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Cellular Networks
  • Change Detection
  • Cloud Computing
  • Computational Science
  • Computer Communications
  • Data Centers
  • Dimensionality Reduction
  • Information Processing
  • Information Science
  • Information Systems
  • Machine Learning
  • Mobile Devices
  • Mobile Phones
  • Network Science
  • Neural Networks
  • Statistical Analysis
  • Wireless Networks

Fields of Study

  • Computer science

Readers

  • Atmospheric Science/Meteorology
  • Computer Networking
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.

Technology Areas

  • Cyber
  • Cyber - Quantum