TY - CHAP
T1 - High-dimensional statistical inference
T2 - Theoretical development to data analytics
AU - Ayyala, Deepak Nag
PY - 2020/1/1
Y1 - 2020/1/1
N2 - 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.
AB - 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.
KW - Asymptotics
KW - Dependent data
KW - High-dimensional inference
KW - Hypothesis testing
KW - Multivariate analysis
KW - Parametric
UR - http://www.scopus.com/inward/record.url?scp=85084598654&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85084598654&partnerID=8YFLogxK
U2 - 10.1016/bs.host.2020.02.003
DO - 10.1016/bs.host.2020.02.003
M3 - Chapter
AN - SCOPUS:85084598654
T3 - Handbook of Statistics
BT - Handbook of Statistics
PB - Elsevier B.V.
ER -