The DOI matches its Crossref record; 51 of the 51 references it cites were found in a registry or an index.
Statistical challenges of high-dimensional data
Open accessAbstract
Modern applications of statistical theory and methods can involve extremely large datasets, often with huge numbers of measurements on each of a comparatively small number of experimental units. New methodology and accompanying theory have emerged in response: the goal of this Theme Issue is to illustrate a number of these recent developments. This overview article introduces the difficulties that arise with high-dimensional data in the context of the very familiar linear statistical model: we give a taste of what can nevertheless be achieved when the parameter vector of interest is sparse, that is, contains many zero elements. We describe other ways of identifying low-dimensional subspaces of the data space that contain all useful information. The topic of classification is then reviewed along with the problem of identifying, from within a very large set, the variables that help to classify observations. Brief mention is made of the visualization of high-dimensional data and ways to handle computational problems in Bayesian analysis are described. At appropriate points, reference is made to the other papers in the issue.
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Methods of integrating data to uncover genotype-phenotype interactions
Ritchie, M. D., Holzinger, E. R., Li, R., Pendergrass, S. A., & Kim, D. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3868 · Cited by 1,085
Computational principles and challenges in single-cell data integration
Argelaguet, R., Cuomo, A. S. E., Stegle, O., & Marioni, J. C. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-00895-7 · Cited by 472
Hallquist, M. N., Wright, A. G. C., & Molenaar, P. C. M. (2021) · Multivariate Behavioral Research · doi:10.1080/00273171.2019.1640103 · Cited by 292
Machine Learning in Pharmacometrics: Opportunities and Challenges
McComb, M., Bies, R., & Ramanathan, M. (2022) · British Journal of Clinical Pharmacology · doi:10.1111/bcp.14801 · Cited by 128
Gubatan, J., Levitte, S., Patel, A., Balabanis, T., Wei, M. T., & Sinha, S. R. (2021) · World Journal of Gastroenterology · doi:10.3748/wjg.v27.i17.1920 · Cited by 121
Metadata
- Authors
- Johnstone, I. M., & Titterington, D. M.
- Venue
- Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
- Publisher
- The Royal Society
- Published
- 2009-11-13
- Volume
- 367
- Issue
- 1906
- Pages
- 4237-4253
- ISSN
- 1364-503X (ISSN-L)
- 1471-2962
- OpenAlex
- W2153491803
- Cited by
- 417
- References
- 51
- FWCI
- 5.19
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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