Data science, big data and statistics

Pedro Galeano () and Daniel Peña
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Pedro Galeano: Universidad Carlos III de Madrid

TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, 2019, vol. 28, issue 2, No 1, 289-329

Abstract: Abstract This article analyzes how Big Data is changing the way we learn from observations. We describe the changes in statistical methods in seven areas that have been shaped by the Big Data-rich environment: the emergence of new sources of information; visualization in high dimensions; multiple testing problems; analysis of heterogeneity; automatic model selection; estimation methods for sparse models; and merging network information with statistical models. Next, we compare the statistical approach with those in computer science and machine learning and argue that the convergence of different methodologies for data analysis will be the core of the new field of data science. Then, we present two examples of Big Data analysis in which several new tools discussed previously are applied, as using network information or combining different sources of data. Finally, the article concludes with some final remarks.

Keywords: Machine learning; Sparse model selection; Statistical learning; Network analysis; Multivariate data; Time series; 62A01; 62H99 (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s11749-019-00651-9

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