The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Statistical Significance of Clustering for High-Dimension, Low–Sample Size Data
Abstract
Clustering methods provide a powerful tool for the exploratory analysis of high-dimension, low–sample size (HDLSS) data sets, such as gene expression microarray data. A fundamental statistical issue in clustering is which clusters are “really there,” as opposed to being artifacts of the natural sampling variation. We propose SigClust as a simple and natural approach to this fundamental statistical problem. In particular, we define a cluster as data coming from a single Gaussian distribution and formulate the problem of assessing statistical significance of clustering as a testing procedure. This Gaussian null assumption allows direct formulation of p values that effectively quantify the significance of a given clustering. HDLSS covariance estimation for SigClust is achieved by a combination of invariance principles, together with a factor analysis model. The properties of SigClust are studied. Simulated examples, as well as an application to a real cancer microarray data set, show that the proposed method works remarkably well for assessing significance of clustering. Some theoretical results also are obtained.
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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
McLachlan, G., & Peel, D. (2000) · Wiley Series in Probability and Statistics · doi:10.1002/0471721182 · Cited by 10,991
The control of the false discovery rate in multiple testing under dependency
Benjamini, Y., & Yekutieli, D. (2001) · The Annals of Statistics · doi:10.1214/aos/1013699998 · Cited by 10,490
Estimating the Number of Clusters in a Data Set Via the Gap Statistic
Tibshirani, R., Walther, G., & Hastie, T. (2001) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/1467-9868.00293 · Cited by 6,205
Cited by
Verhaak, R. G. W., Hoadley, K. A., Purdom, E., Wang, V., Qi, Y., Wilkerson, M. D., Miller, C. R., Ding, L., Golub, T., Mesirov, J. P., et al. (2010) · Cancer Cell · doi:10.1016/j.ccr.2009.12.020 · Cited by 7,575
Self-citationSupervised Risk Predictor of Breast Cancer Based on Intrinsic Subtypes
Parker, J. S., Mullins, M., Cheang, M. C. U., Leung, S., Voduc, D., Vickery, T., Davies, S., Fauron, C., He, X., Hu, Z., et al. (2009) · Journal of Clinical Oncology · doi:10.1200/jco.2008.18.1370 · Cited by 4,721
Self-citationTsoi, J., Robert, L., Paraiso, K., Galvan, C., Sheu, K. M., Lay, J., Wong, D. J. L., Atefi, M., Shirazi, R., Wang, X., et al. (2018) · Cancer Cell · doi:10.1016/j.ccell.2018.03.017 · Cited by 938
Large Covariance Estimation by Thresholding Principal Orthogonal Complements
Fan, J., Liao, Y., & Mincheva, M. (2013) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/rssb.12016 · Cited by 909
Hennessy, B. T., Gonzalez-Angulo, A.-M., Stemke-Hale, K., Gilcrease, M. Z., Krishnamurthy, S., Lee, J.-S., Fridlyand, J., Sahin, A., Agarwal, R., Joy, C., et al. (2009) · Cancer Research · doi:10.1158/0008-5472.can-08-3441 · Cited by 874
Metadata
- Authors
- Liu, Y., Hayes, D. N., Nobel, A., & Marron, J. S.
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 2008-09
- Volume
- 103
- Issue
- 483
- Pages
- 1281-1293
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W1991767154
- Cited by
- 267
- References
- 4
- FWCI
- 15.90
- Type
- journal article
- Language
- en
- Open access
- closed
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