The role of statistics in the data revolution?
Abstract
Summary The nature of data is rapidly changing. Data sets are becoming increasingly large and complex. Modern methodology for analyzing these new types of data are emerging from the fields of Data Base Managment, Artificial Intelligence, Machine Learning, Pattern Recognition, and Data Visualization. So far Statistics as a field has played a minor role. This paper explores some of the reasons for this, and why statisticians should have an interest in participating in the development of new methods for large and complex data sets.
Integrity signals
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Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Statistical models start with an assumption about the underlying data distribution, such as Gaussian or Poisson.
“Statistical models start with an assumption about the underlying data distribution (e.g.Gaussian, Poisson).”
From Applications of machine learning in animal behaviour studies (2017) · cites this paper as Friedman, 2001
Statistical modelling and machine learning both seek to build a mathematical description, a model, of the data and the underlying mechanism it represents, and therefore substantially overlap.
“Both statistical modelling and ML seek to build a mathematical description, a model, of the data and the underlying mechanism it represents; thus inevitably there is substantial overlap between the two”
From Applications of machine learning in animal behaviour studies (2017) · cites this paper as Friedman, 2001
For statistical models, the focus is on inference: estimating the parameters of the statistical model that most likely gave rise to the observed data and providing uncertainty bounds for these estimates.
“The focus is on inference; estimating the parameters of the statistical model that most likely gave rise to the observed data, and providing uncertainty bounds for these estimates.”
From Applications of machine learning in animal behaviour studies (2017) · cites this paper as Friedman, 2001
For machine learning, the focus is typically on prediction; without necessarily assuming a functional distribution for the data, a model that achieves optimal predictive performance is identified.
“For ML, the focus is typically on prediction; without necessarily assuming a functional distribution for the data, a model that achieves optimal predictive performance is identified.”
From Applications of machine learning in animal behaviour studies (2017) · cites this paper as Friedman, 2001
Traditional statistical modelling puts forward a hypothesis and accepts or rejects it depending on consistency with measured observations, whereas machine learning methods learn the hypothesis directly from the training data set.
“While in traditional statistical modelling a hypothesis (model) is put forward and is then accepted/rejected depending on how consistent it is with the measured observations, ML methods learn this hypothesis directly from the training data set.”
From Applications of machine learning in animal behaviour studies (2017) · cites this paper as Friedman, 2001
Cites
Tukey, J. W. (1962) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177704711 · Cited by 1,934 · see citing works
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A Direct Approach to False Discovery Rates
Storey, J. D. (2002) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/1467-9868.00346 · Cited by 5,603 · see citing works
Applications of machine learning in animal behaviour studies
Valletta, J. J., Torney, C., Kings, M., Thornton, A., & Madden, J. (2017) · Animal Behaviour · doi:10.1016/j.anbehav.2016.12.005 · Cited by 505 · see citing works
“Both statistical modelling and ML seek to build a mathematical description, a model, of the data and the underlying mechanism it represents; thus inevitably there is substantial overlap between the two (Breiman, 2001b; Friedman, 2001; Zoubin Ghahramani, 2015).”
- Authors
- Friedman, J. H.
- Affiliations
- Stanford University (US)
- Corresponding author
- Friedman, Jerome H.
- ORCID
- Friedman, Jerome H.
- Venue
- International Statistical Review
- Publisher
- Wiley
- Published
- 2001-04
- Volume
- 69
- Issue
- 1
- Pages
- 5-10
- ISSN
- 0306-7734 (ISSN-L)
- 1751-5823
- OpenAlex
- W2115090310
- Cited by
- 64
- References
- 1
- Citations by year
- 2026: 1 · 2024: 1 · 2023: 2 · 2022: 4 · 2021: 1 · 2020: 4 · 2019: 3 · 2018: 3 · 2017: 5 · 2015: 1 · 2014: 2 · 2013: 2 · 2012: 4
- FWCI
- 12.26
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Statistical Review (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Keywords
- data revolution, large data sets, complex data, statistical methodology, machine learning, artificial intelligence, data visualization, pattern recognition, database management, role of statisticians
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