Data science, big data and statistics
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 pro...
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| Published in | Test (Madrid, Spain) Vol. 28; no. 2; pp. 289 - 329 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.06.2019
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
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| 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. |
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| AbstractList | 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. |
| Author | Peña, Daniel Galeano, Pedro |
| Author_xml | – sequence: 1 givenname: Pedro orcidid: 0000-0003-2577-2747 surname: Galeano fullname: Galeano, Pedro email: pedro.galeano@uc3m.es organization: Departamento de Estadística and Institute of Financial Big Data, Universidad Carlos III de Madrid – sequence: 2 givenname: Daniel surname: Peña fullname: Peña, Daniel organization: Departamento de Estadística and Institute of Financial Big Data, Universidad Carlos III de Madrid |
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| CitedBy_id | crossref_primary_10_1214_20_BJPS466 crossref_primary_10_1007_s11837_021_04697_9 crossref_primary_10_3390_pr7080493 crossref_primary_10_1007_s00362_021_01279_4 crossref_primary_10_1016_j_techfore_2021_121111 crossref_primary_10_56294_sctconf2023400 crossref_primary_10_3390_app13031666 crossref_primary_10_2139_ssrn_4825666 crossref_primary_10_1007_s13042_021_01359_9 crossref_primary_10_1016_j_dim_2022_100020 |
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| Keywords | Multivariate data Statistical learning Network analysis 62A01 Machine learning Time series Sparse model selection 62H99 |
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| SubjectTerms | Big Data Data analysis Data management Data science Economics Finance Insurance Invited Paper Machine learning Management Mathematics and Statistics Statistical methods Statistical models Statistical Theory and Methods Statistics Statistics for Business |
| Title | Data science, big data and statistics |
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