Statistical Inference for High Dimensional Data
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "D1: Probability and Statistics".
Deadline for manuscript submissions: closed (31 October 2024) | Viewed by 575
Special Issue Editor
Interests: high dimensional statistical inference; statistical analysis for longitudinal and functional data; empirical likelihood method; nonparametric smoothing methods; missing data problems
Special Issue Information
Dear Colleagues,
Statistical inference is the process of using data analysis to infer the properties of an underlying distribution of probability. The field of high-dimensional statistics studies data whose dimension is larger than typically considered in classical multivariate analysis. The area arose owing to the emergence of many modern data sets in which the dimension of the data vectors may be comparable to, or even larger than, the sample size so that justification for the use of traditional techniques, often based on asymptotic arguments with the dimension held fixed as the sample size increased, was lacking. However, in modern-day analytics, there is an ever-growing need to develop statistical models to study large data sets, i.e., high-dimensional data. Several approaches have been developed so far between dimension reduction, asymptotic-driven methods, and random projection-based methods. Estimation and hypothesis testing for mean and covariance matrices have been extensively studied for high-dimensional parametric models. However, the practical implementation of these methods is limited and is primarily restricted to researchers involved in high-dimensional inference. With several applied fields, such as genomics, metagenomics, and social networking, the high-dimensional inference is a key component of big data analytics.
This Special Issue aims to gather recent methods and results of high-dimensional statistical inference. Both original research and review papers are encouraged.
Dr. Ping-Shou Zhong
Guest Editor
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Keywords
- multivariate analysis
- inferential statistical analysis
- high-dimensional inference
- likelihood inference
- hypothesis testing
- random vectors
- variable selection
- high-dimensional classification
- dependent data
- machine learning
- big data analytics
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