High-dimensional statistical inference: Theoretical development to data analytics

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

In modern-day analytics, there is ever-growing need to develop statistical models to study large data sets, i.e., high-dimensional data. Between dimension reduction, asymptotics-driven methods and random projection-based methods, there are several approaches developed so far. For high-dimensional parametric models, estimation and hypothesis testing for mean and covariance matrices have been extensively studied. However, the practical implementation of these methods is fairly limited and is primarily restricted to researchers involved in high-dimensional inference. With several applied fields such as genomics, metagenomics and social networking, high-dimensional inference is a key component of big data analytics. In this chapter, a comprehensive overview of high-dimensional inference and its applications in data analytics is provided. Key theoretical developments and computational tools are presented, giving readers an in-depth understanding of challenges in big data analysis.

Original languageEnglish (US)
Title of host publicationHandbook of Statistics
PublisherElsevier B.V.
DOIs
StateAccepted/In press - Jan 1 2020

Publication series

NameHandbook of Statistics
ISSN (Print)0169-7161

Keywords

  • Asymptotics
  • Dependent data
  • High-dimensional inference
  • Hypothesis testing
  • Multivariate analysis
  • Parametric

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation
  • Applied Mathematics

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