Review of statistical analysis methods of high-dimensional data
DOI:
https://doi.org/10.15587/1729-4061.2015.51603Keywords:
big data, statistical analysis, statistical analysis methods, bootstrap, resamplingAbstract
We live in the era of "big data". Big data opens up new opportunities for modern society, has become the "raw material" for production, a new source for immense economic and social value. At the same time, big data has set new computational and statistical tasks before the researcher. In order to study the status of these tasks, the paper describes the main applications of big data, investigates the statistical computing problems, associated with a large volume, diversity and high speed, affecting a paradigm shift of statistical and computational methods. A review of existing statistical methods, algorithms and research in recent years is presented. The research results show that several factors require the development of new, more effective statistical methods and algorithms: firstly, traditional statistical methods are not justified in terms of statistical significance with respect to big data; secondly, in terms of computational efficiency; the third factor is relevant to the specific features inherent in big data: heterogeneity, the accumulation of noise, spurious correlations, etc. It appears that this area will continue to be the subject of research.
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