# High dimensional statistical inference: theoretical development to data analytics

## TL;DR

This paper provides a comprehensive overview of high dimensional statistical inference, covering theoretical developments and computational tools essential for big data analytics in various applied fields.

## Contribution

It offers an in-depth synthesis of recent advances in high dimensional inference methods and their practical applications in data analytics.

## Key findings

- Summarizes key theoretical developments in high dimensional inference.
- Discusses computational tools for big data analysis.
- Highlights challenges and solutions in high dimensional data applications.

## Abstract

This article is due to appear in the Handbook of Statistics, Vol. 43, Elsevier/North-Holland, Amsterdam, edited by Arni S. R. Srinivasa Rao and C. R. Rao. In modern day analytics, there is ever growing need to develop statistical models to study 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, practical implementation of these methods are fairly limited and are 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.