Cited by 1,482Big Data Technologies and ApplicationsStatistical Methods and InferenceStatistical Methods and Applications

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Challenges of Big Data analysis

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Fan, Jianqing, Han, Fang, Liu, Han·2014·National Science Review

DOI 10.1093/nsr/nwt032PMC PMC4236847

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Abstract

Big Data bring new opportunities to modern society and challenges to data scientists. On the one hand, Big Data hold great promises for discovering subtle population patterns and heterogeneities that are not possible with small-scale data. On the other hand, the massive sample size and high dimensionality of Big Data introduce unique computational and statistical challenges, including scalability and storage bottleneck, noise accumulation, spurious correlation, incidental endogeneity and measurement errors. These challenges are distinguished and require new computational and statistical paradigm. This paper gives overviews on the salient features of Big Data and how these features impact on paradigm change on statistical and computational methods as well as computing architectures. We also provide various new perspectives on the Big Data analysis and computation. In particular, we emphasize on the viability of the sparsest solution in high-confidence set and point out that exogenous assumptions in most statistical methods for Big Data cannot be validated due to incidental endogeneity. They can lead to wrong statistical inferences and consequently wrong scientific conclusions.

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Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
Convex Optimization
Boyd, S., & Vandenberghe, L. (2004) · doi:10.1017/cbo9780511804441 · Cited by 56,601
A new look at the statistical model identification
Akaike, H. (1974) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1974.1100705 · Cited by 51,879
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482
Matrix Computations
Golub, G. (2012) · doi:10.56021/9781421407944 · Cited by 34,505
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162
Compressed sensing
Donoho, D. L. (2006) · IEEE Transactions on Information Theory · doi:10.1109/tit.2006.871582 · Cited by 23,994
Longitudinal data analysis using generalized linear models
LIANG, K.-Y., & ZEGER, S. L. (1986) · Biometrika · doi:10.1093/biomet/73.1.13 · Cited by 18,550
Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
Boyd, S., Parikh, N., Chu, E., Peleato, B., & Eckstein, J. (2011) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000016 · Cited by 17,075

Flagged first, then by citations

Show all 144 references

Research works printed but not linked: 6

Donoho, High-dimensional data analysis: the curses and blessings of dimensionality
Bickel, 2008, Discussion on the paper ‘Sure independence screening for ultrahigh dimensional feature space’ by Fan and Lv, J Roy Stat Soc B, 70, 883
Friedman, 2003, Gradient directed regularization for linear regression and classification, Technical report
Milham, 2011, The International Neuroimaging Data-sharing Initiative (INDI) and the Functional Connectomes Project, 17th Annual Meeting of the Organization for Human Brain Mapping
2012, The ADHD-200 Consortium. The ADHD-200 consortium: a model to advance the translational potential of neuroimaging in clinical neuroscience, Front Syst Neurosci, 6, 62
Cochrane, 2001, Asset Pricing

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Authors: Fan, J., Han, F., & Liu, H.
Affiliations: Department of Operations Research and Financial Engineering, Princeton University, Princeton, NJ 08544, USA;
Johns Hopkins University (US)
Princeton University (US)
ORCID: Han, Fang
Liu, Han
Venue: National Science Review
Publisher: Oxford University Press (OUP)
Published: 2014-02-05
Volume: 1
Issue: 2
Pages: 293-314
ISSN: 2053-714X (ISSN-L)
2095-5138
DOI: 10.1093/nsr/nwt032
PMC: PMC4236847
OpenAlex: W2114060717
Cited by: 1,482
References: 144
Citations by year: 2026: 86 · 2025: 132 · 2024: 142 · 2023: 172 · 2022: 118 · 2021: 122 · 2020: 125 · 2019: 157 · 2018: 162 · 2017: 120 · 2016: 90 · 2015: 40 · 2014: 15
FWCI: 165.47
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor, 973 d)
Locations: National Science Review (publishedVersion, cc-by)
PubMed (publishedVersion)
Europe PMC (PubMed Central) (acceptedVersion)
PubMed Central (acceptedVersion)
arXiv (Cornell University) (submittedVersion)
HighWire Press Open Archive (submittedVersion)
arXiv (Cornell University)
Funders: National Science Foundation (R01-GM072611, 1206464, 1332109, 1116730)
National Institutes of Health
National Institute of Mental Health (R01MH102339)
National Institute of General Medical Sciences (R01GM072611, R01GM100474)
Topics: Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
Keywords: big data analytics, high-dimensional data, scalability, noise accumulation, spurious correlations, measurement error, statistical inference, computer architecture

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